SAA-C03 Practice Exam Questions with Answers AWS Certified Solutions Architect - Associate (SAA-C03) Certification

This option is the best solution because it allows the company to run its payment application on AWS with minimal operational overhead and infrastructure management. By using Amazon API Gateway, the company can create a secure and scalable API to receive payment notifications from mobile devices. By using AWS Lambda, the company can run a serverless function to validate the payment notifications and send them to the backend application. Lambda handles the provisioning, scaling, and security of the function, reducing the operational complexity and cost. By using Amazon ECS with AWS Fargate, the company can run the backend application on a fully managed container service that scales the compute resources automatically and does not require any EC2 instances to manage. Fargate allocates the right amount of CPU and memory for each container and adjusts them as needed.

A. Create an Amazon Simple Queue Service (Amazon SQS) queue Integrate the queue with an Amazon EventBndge rule to receive payment notifications from mobile devices Configure the rule to validate payment notifications and send the notifications to the backend application Deploy the backend application on Amazon Elastic Kubernetes Service (Amazon EKS) Anywhere Create a standalone cluster. This option is not optimal because it requires the company to manage the Kubernetes cluster that runs the backend application. Amazon EKS Anywhere is a deployment option that allows the company to create and operate Kubernetes clusters on-premises or in other environments outside AWS. The company would need to provision, configure, scale, patch, and monitor the cluster nodes, which can increase the operational overhead and complexity. Moreover, the company would need to ensure the connectivity and security between the AWS services and the EKS Anywhere cluster, which can also add challenges and risks.

B. Create an Amazon API Gateway API Integrate the API with anAWS Step Functions state ma-chine to receive payment notifications from mobile devices Invoke the state machine to validate payment notifications and send the notifications to the backend application Deploy the backend application on Amazon Elastic Kubernetes Sen/ice (Amazon EKS). Configure an EKS cluster with self-managed nodes. This option is not ideal because it requires the company to manage the EC2 instances that host the Kubernetes cluster that runs the backend application. Amazon EKS is a fully managed service that runs Kubernetes on AWS, but it still requires the company to manage the worker nodes that run the containers. The company would need to provision, configure, scale, patch, and monitor the EC2 instances, which can increase the operational overhead and infrastructure costs. Moreover, using AWS Step Functions to validate the payment notifications may be unnecessary and complex, as the validation logic can be implemented in a simpler way with Lambda or other services.

C. Create an Amazon Simple Queue Sen/ice (Amazon SQS) queue Integrate the queue with an Amazon EventBridge rule to receive payment notifications from mobile devices Configure the rule to validate payment notifications and send the notifications to the backend application Deploy the backend application on Amazon EC2 Spot Instances Configure a Spot Fleet with a default al-location strategy. This option is not cost-effective because it requires the company to manage the EC2 instances that run the backend application. The company would need to provision, configure, scale, patch, and monitor the EC2 instances, which can increase the operational overhead and infrastructure costs. Moreover, using Spot Instances can introduce the risk of interruptions, as Spot Instances are reclaimed by AWS when the demand for On-Demand Instances increases. The company would need to handle the interruptions gracefully and ensure the availability and reliability of the backend application.

References:

1 Amazon API Gateway - Amazon Web Services

2 AWS Lambda - Amazon Web Services

3 Amazon Elastic Container Service - Amazon Web Services

4 AWS Fargate - Amazon Web Services

This option is the most suitable way to improve the game’s metadata load times without migrating the database. Amazon ElastiCache for Redis is a fully managed, in-memory data store that provides sub-millisecond latency and high throughput for read-intensive workloads. You can use it as a caching layer in front of your RDS DB instance to store frequently accessed metadata and reduce the load on the database. You can also take advantage of Redis features such as snapshots, replication, and data persistence to ensure data durability and availability. ElastiCache for Redis scales automatically to meet your demand and integrates with other AWS services such as CloudFormation, CloudWatch, and IAM.

Option A is not optimal because migrating the database to Amazon Aurora with Aurora Replicas would incur additional costs and complexity. Amazon Aurora is a relational database service that provides high performance, availability, and compatibility with MySQL and PostgreSQL. Aurora Replicas are read-only copies of the primary database that can be used for scaling read capacity and enhancing availability. However, migrating the database to Aurora would require modifying the application code, testing the compatibility, and performing the data migration. Moreover, Aurora Replicas may not provide sub-millisecond response times as ElastiCache for Redis does.

Option B is not optimal because migrating the database to Amazon DynamoDB with global tables would incur additional costs and complexity. Amazon DynamoDB is a NoSQL database service that provides fast and flexible data access for any scale. Global tables are a feature of DynamoDB that enables you to replicate your data across multiple AWS Regions for high availability and performance. However, migrating the database to DynamoDB would require changing the data model, modifying the application code, and performing the data migration. Moreover, global tables may not be necessary for the game’s metadata, as they are mainly used for cross-region data access and disaster recovery.

Option D is not optimal because adding an Amazon ElastiCache for Memcached layer in front of the database would not provide the same capabilities as ElastiCache for Redis. Amazon ElastiCache for Memcached is another fully managed, in-memory data store that provides high performance and scalability for caching workloads. However, Memcached does not support snapshots, replication, or data persistence, which means that the cached data may be lost in case of a node failure or a cache eviction. Moreover, Memcached does not integrate with other AWS services as well as Redis does. Therefore, ElastiCache for Redis is a better choice for this scenario. References:

What Is Amazon ElastiCache for Redis?

What Is Amazon Aurora?

What Is Amazon DynamoDB?

What Is Amazon ElastiCache for Memcached?

Amazon Inspector is an automated security assessment service that helps improve the security and compliance of applications deployed on AWS. Amazon Inspector automatically assesses applications for exposure, vulnerabilities, and deviations from best practices. After performing an assessment, Amazon Inspector produces a detailed list of security findings prioritized by level of severity1. Amazon Inspector can scan the EC2 instances for software vulnerabilities and provide a report of each instance’s patch status. AWS Systems Manager Patch Manager is a capability of AWS Systems Manager that automates the process of patching managed nodes with both security-related updates and other types of updates. Patch Manager uses patch baselines, which include rules for auto-approving patches within days of their release, in addition to optional lists of approved and rejected patches. Patch Manager can patch fleets of Amazon EC2 instances, edge devices, on-premises servers, and virtual machines (VMs) by operating system type2. Patch Manager can patch the EC2 instances on a regular schedule and provide a report of each instance’s patch status. Therefore, the combination of Amazon Inspector and AWS Systems Manager Patch Manager will meet the requirements of the question.

The other options are not valid because:

Amazon Macie is a security service that uses machine learning to automatically discover, classify, and protect sensitive data in AWS. Amazon Macie does not scan the EC2 instances for software vulnerabilities, but rather for data classification and protection3. A cron job is a Linux command for scheduling a task to be executed sometime in the future. A cron job is not a reliable way to patch the EC2 instances on a regular schedule, as it may fail or be interrupted by other processes4.

Amazon GuardDuty is a threat detection service that continuously monitors for malicious activity and unauthorized behavior to protect your AWS accounts and workloads. Amazon GuardDuty does not scan the EC2 instances for software vulnerabilities, but rather for network and API activity anomalies5. AWS Systems Manager Session Manager is a fully managed AWS Systems Manager capability that lets you manage your Amazon EC2 instances, edge devices, on-premises servers, and virtual machines (VMs) through an interactive one-click browser-based shell or the AWS Command Line Interface (AWS CLI). Session Manager does not patch the EC2 instances on a regular schedule, but rather provides secure and auditable node management2.

Amazon Detective is a security service that makes it easy to analyze, investigate, and quickly identify the root cause of potential security issues or suspicious activities. Amazon Detective does not scan the EC2 instances for software vulnerabilities, but rather collects and analyzes data from AWS sources such as Amazon GuardDuty, Amazon VPC Flow Logs, and AWS CloudTrail. Amazon EventBridge is a serverless event bus that makes it easy to connect applications using data from your own applications, integrated Software-as-a-Service (SaaS) applications, and AWS services. EventBridge delivers a stream of real-time data from event sources, such as Zendesk, Datadog, or Pagerduty, and routes that data to targets like AWS Lambda. EventBridge does not patch the EC2 instances on a regular schedule, but rather triggers actions based on events.

References: Amazon Inspector, AWS Systems Manager Patch Manager, Amazon Macie, Cron job, Amazon GuardDuty, [Amazon Detective], [Amazon EventBridge]

This answer is the most secure and recommended option for securing the root user of a new AWS account. The root user is the identity that has complete access to all AWS services and resources in the account. It is accessed by signing in with the email address and password that were used to create the account. To protect the root user credentials from unauthorized use, AWS advises the following best practices:

Create IAM users for daily administrative tasks. IAM users are identities that you create in your account that have specific permissions to access AWS resources. You can create individual IAM users for yourself and for others who need access to your account. You can also assign IAM users to IAM groups that have a set of policies that grant permissions to perform common tasks. By using IAM users instead of the root user, you can follow the principle of least privilege and reduce the risk of compromising your account.

Enable multi-factor authentication (MFA) on the root user. MFA is a security feature that requires users to prove their identity by providing two pieces of information: their password and a code from a device that only they have access to. By enabling MFA on the root user, you can add an extra layer of protection to your account and prevent unauthorized access even if your password is compromised.

Limit the tasks you perform with the root user account. You should use the root user only for tasks that require root user credentials, such as changing your account settings, closing your account, or managing consolidated billing. For a complete list of tasks that require root user credentials, see Tasks that require root user credentials. For all other tasks, you should use IAM users or roles that have the appropriate permissions.

References:

AWS account root user

Root user best practices for your AWS account

Tasks that require root user credentials

The company wants to improve the availability and performance of the application, as well as protect it against common web exploits. The company also needs static IP addresses for the application. To meet these requirements, a solutions architect should recommend the following solution:

Put the EC2 instances behind Network Load Balancers (NLBs) in each Region. NLBs are designed to handle millions of requests per second while maintaining high throughput at ultra-low latency. NLBs also support static IP addresses for each Availability Zone, which can be useful for whitelisting or firewalling purposes.

Deploy AWS WAF on the NLBs. AWS WAF is a web application firewall that helps protect web applications from common web exploits that could affect availability, security, or performance. AWS WAF lets you define customizable web security rules that control which traffic to allow or block to your web applications.

Create an accelerator using AWS Global Accelerator and register the NLBs as endpoints. AWS Global Accelerator is a service that improves the availability and performance of your applications with local or global users. It provides static IP addresses that act as a fixed entry point to your application endpoints in any AWS Region. It uses the AWS global network to optimize the path from your users to your applications, improving the performance of your TCP and UDP traffic.

This solution will provide high availability across Availability Zones and Regions, improve performance by routing traffic over the AWS global network, protect the application from common web attacks, and provide static IP addresses for the application.

References:

Network Load Balancer

AWS WAF

AWS Global Accelerator

This solution meets the requirements because it allows the company to use a single Direct Connect connection to connect to multiple VPCs in different Regions using a Direct Connect gateway. A Direct Connect gateway is a globally available resource that enables you to connect your on-premises network to VPCs in any AWS Region, except the AWS China Regions. You can associate a Direct Connect gateway with a transit gateway or a virtual private gateway in each Region. By routing traffic from the virtual private gateways of the VPCs to the Direct Connect gateway, you can enable inter-Region and on-premises connectivity for your VPCs. This solution is scalable because you can add more VPCs in different Regions to the Direct Connect gateway without creating additional connections. This solution also reduces operational overhead because you do not need to manage multiple VPN appliances, VPN connections, or VPC peering connections.

References:

Direct Connect gateways

Inter-Region VPC peering

The most suitable solution for the company’s requirements is to set the home AWS Region in AWS Migration Hub and use AWS Application Discovery Service to collect data about the on-premises servers. This solution will enable the company to gather usage and configuration data of its on-premises servers and workloads, and plan a migration to AWS.

AWS Migration Hub is a service that simplifies and accelerates migration tracking by aggregating migration status information into a single console. Users can view the discovered servers, group them into applications, and track the migration status of each application from the Migration Hub console in their home Region. The home Region is the AWS Region where users store their migration data, regardless of which Regions they migrate into1.

AWS Application Discovery Service is a service that helps users plan their migration to AWS by collecting usage and configuration data about their on-premises servers and databases. Application Discovery Service is integrated with AWS Migration Hub and supports two methods of performing discovery: agentless discovery and agent-based discovery. Agentless discovery can be performed by deploying the Application Discovery Service Agentless Collector through VMware vCenter, which collects static configuration data and utilization data for virtual machines (VMs) and databases. Agent-based discovery can be performed by deploying the AWS Application Discovery Agent on each of the VMs and physical servers, which collects static configuration data, detailed time-series system-performance information, inbound and outbound network connections, and processes that are running2.

The other options are not correct because they do not meet the requirements or are not relevant for the use case. Using the AWS Schema Conversion Tool (AWS SCT) to create the relevant templates and using AWS Trusted Advisor to collect data about the on-premises servers is not correct because this solution is not suitable for collecting usage and configuration data of on-premises servers and workloads. AWS SCT is a tool that helps users convert database schemas and code objects from one database engine to another, such as from Oracle to PostgreSQL3. AWS Trusted Advisor is a service that provides best practice recommendations for cost optimization, performance, security, fault tolerance, and service limits4. Using the AWS Schema Conversion Tool (AWS SCT) to create the relevant templates and using AWS Database Migration Service (AWS DMS) to collect data about the on-premises servers is not correct because this solution is not suitable for collecting usage and configuration data of on-premises servers and workloads. As mentioned above, AWS SCT is a tool that helps users convert database schemas and code objects from one database engine to another. AWS DMS is a service that helps users migrate relational databases, non-relational databases, and other types of data stores to AWS with minimal downtime5.

References:

Home Region - AWS Migration Hub

What is AWS Application Discovery Service? - AWS Application Discovery Service

AWS Schema Conversion Tool - Amazon Web Services

What Is Trusted Advisor? - Trusted Advisor

What Is AWS Database Migration Service? - AWS Database Migration Service

AWS Snowball is a petabyte-scale data transport service that uses secure devices to transfer large amounts of data into and out of the AWS Cloud. Snowball addresses common challenges with large-scale data transfers including high network costs, long transfer times, and security concerns. AWS Snowball can transfer up to 80 TB of data per device, and multiple devices can be used in parallel to meet the migration deadline. AWS Snowball is more cost-effective than AWS Snowmobile, which is designed for exabyte-scale data transfers, or Amazon S3 Transfer Acceleration, which is optimized for fast transfers over long distances. Amazon S3 VPC endpoint does not increase the upload speed, but only provides a secure and private connection between the VPC and S3. References: AWS Snowball, AWS Snowmobile, Amazon S3 Transfer Acceleration, Amazon S3 VPC endpoint

This solution meets the requirements because it uses SCPs to restrict the actions that can be performed on cost usage tags in the organization. SCPs are a type of organization policy that you can use to manage permissions in your organization. SCPs specify the maximum permissions for an organization, organizational unit (OU), or account. You can use SCPs to enforce consistent tag policies across your organization and prevent unauthorized or accidental changes to your tags. You can also create exceptions for authorized principals, such as administrators or auditors, who need to modify tags for legitimate purposes.

References:

Service control policies (SCPs) - AWS Organizations

Tag policies - AWS Organizations

These two steps will meet the requirements because they will provide high availability and scalability for the web application without rewriting it. Amazon EC2 Auto Scaling allows you to automatically adjust the number of EC2 instances in response to changes in demand. By configuring Auto Scaling with multiple Availability Zones in private subnets, you can ensure that your web application is distributed across isolated and fault-tolerant locations, and that your instances are not directly exposed to the internet. An Application Load Balancer operates at the application layer and distributes incoming web traffic across multiple targets, such as EC2 instances, containers, or Lambda functions. By configuring an Application Load Balancer in a public subnet, you can enable your web application to handle requests from the internet and route them to the appropriate targets in the private subnets.

References:

What is Amazon EC2 Auto Scaling?

What is an Application Load Balancer?

AWS Organizations: AWS Organizations allows you to create multiple AWS accounts and manage them centrally. You can organize accounts into organizational units (OUs) and apply policies to these units.

Organizational Units (OUs):

Create separate OUs for each department: finance, data analytics, and development.

Place the respective AWS accounts for each department into their corresponding OUs.

Service Control Policies (SCPs):

SCPs are policies that can restrict which AWS services and actions are available to accounts in an OU.

Create SCPs to define which services each department can use and attach these policies to the appropriate OUs.

SCPs apply to all IAM users, groups, and roles within the accounts in the OU, providing centralized control over service usage.

Operational Efficiency: Using AWS Organizations and SCPs provides a scalable and centralized way to manage permissions across multiple accounts with minimal operational overhead.

References:

AWS Organizations

Service Control Policies

This solution meets the requirements because it requires the least amount of infrastructure management and guarantees exactly-once delivery for application messaging. AWS Lambda is a serverless compute service that lets you run code without provisioning or managing servers. You only pay for the compute time you consume. Lambda scales automatically with the size of your workload. Amazon SQS FIFO queues are designed to ensure that messages are processed exactly once, in the exact order that they are sent. FIFO queues have high availability and deliver messages in a strict first-in, first-out order. You can use Amazon SQS to decouple and scale microservices, distributed systems, and serverless applications. References: AWS Lambda, Amazon SQS FIFO queues

Amazon Cognito user pools provide a secure and scalable user directory for user authentication and management. User pools support various authentication methods, such as username and password, email and password, phone number and password, and social identity providers. User pools also support multi-factor authentication (MFA), which adds an extra layer of security by requiring users to provide a verification code or a biometric factor in addition to their credentials. User pools can also enable risk-based adaptive authentication, which dynamically adjusts the authentication challenge based on the risk level of the sign-in attempt. For example, if a user tries to sign in from an unfamiliar device or location, the user pool can require a stronger authentication factor, such as SMS or email verification code. This feature helps to protect user accounts from unauthorized access and reduce the friction for legitimate users. User pools can scale up to millions of users and integrate with other AWS services, such as Amazon SNS, Amazon SES, AWS Lambda, and AWS KMS.

Amazon Cognito identity pools provide a way to federate identities from multiple identity providers, such as user pools, social identity providers, and corporate identity providers. Identity pools allow users to access AWS resources with temporary, limited-privilege credentials. Identity pools do not provide user authentication or management features, such as MFA or adaptive authentication. Therefore, option B is not correct.

AWS Identity and Access Management (IAM) is a service that helps to manage access to AWS resources. IAM users are entities that represent people or applications that need to interact with AWS. IAM users can be authenticated with a password or an access key. IAM users can also enable MFA for their own accounts, by using the AllowManageOwnUserMFA action in an IAM policy. However, IAM users are not suitable for user authentication for web or mobile applications, as they are intended for administrative purposes. IAM users also do not support adaptive authentication based on risk factors. Therefore, option C is not correct.

AWS IAM Identity Center (AWS Single Sign-On) is a service that enables users to sign in to multiple AWS accounts and applications with a single set of credentials. AWS SSO supports various identity sources, such as AWS SSO directory, AWS Managed Microsoft AD, and external identity providers. AWS SSO also supports MFA for user authentication, which can be configured in the permission sets that define the level of access for each user. However, AWS SSO does not support adaptive authentication based on risk factors. Therefore, option D is not correct.

References:

Amazon Cognito User Pools

Adding Multi-Factor Authentication (MFA) to a User Pool

Risk-Based Adaptive Authentication

Amazon Cognito Identity Pools

IAM Users

Enabling MFA Devices

AWS Single Sign-On

How AWS SSO Works

This solution meets the following requirements:

It is cost-effective, as it only uses serverless components that are charged based on usage and do not require any upfront provisioning or maintenance.

It is scalable, as it can handle any number of recipe records that are uploaded to the S3 bucket without any performance degradation or manual intervention.

It is easy to implement, as it does not require any machine learning knowledge or complex data processing logic. Amazon Comprehend is a natural language processing service that can automatically extract entities such as ingredients from text files. The Lambda function can simply invoke the Comprehend API and store the results in the DynamoDB table.

It is reliable, as it can handle non-food records and errors gracefully. Amazon Comprehend can detect the language and domain of the text files and return an appropriate response. The Lambda function can also implement error handling and logging mechanisms to ensure the data quality and integrity.

References:

Using AWS Lambda with Amazon S3 - AWS Lambda

What Is Amazon Comprehend? - Amazon Comprehend

Working with Tables - Amazon DynamoDB

The AWS Fargate launch type is a serverless way to run containers on Amazon ECS, without having to manage any underlying infrastructure. You only pay for the resources required to run your containers, and AWS handles the provisioning, scaling, and security of the cluster. Amazon EFS is a fully managed, elastic, and scalable file system that can be mounted to multiple containers, and provides high availability and durability. By using AWS Fargate and Amazon EFS, you can run your Docker container image with 50 GB of storage available for temporary files, with the least operational overhead. This solution meets the requirements of the question.

References:

AWS Fargate

Amazon Elastic File System

Using Amazon EFS file systems with Amazon ECS

To satisfy the security requirements, the solutions architect should download AWS-provided root certificates and provide the certificates in all connections to the RDS instance. This will enable SSL/TLS encryption for data in transit between the application and the RDS instance. SSL/TLS encryption provides a layer of security by encrypting data that moves between the client and the server. Amazon RDS creates an SSL certificate and installs the certificate on the DB instance when the instance is provisioned. The application can use the AWS-provided root certificates to verify the identity of the DB instance and establish a secure connection1.

The other options are not correct because they do not enable encryption for data in transit or are not relevant for the use case. Enabling IAM database authentication on the database is not correct because this option only provides a method of authentication, not encryption. IAM database authentication allows users to use AWS Identity and Access Management (IAM) users and roles to access a database, instead of using a database user name and password2. Providing self-signed certificates is not correct because this option is not secure or reliable. Self-signed certificates are certificates that are signed by the same entity that issued them, instead of by a trusted certificate authority (CA). Self-signed certificates can be easily forged or compromised, and are not recognized by most browsers and applications3. Taking a snapshot of the RDS instance and restoring it to a new instance with encryption enabled is not correct because this option only enables encryption at rest, not encryption in transit. Encryption at rest protects data that is stored on disk, but does not protect data that is moving between the client and the server4.

References:

Using SSL/TLS to encrypt a connection to a DB instance - Amazon Relational Database Service

IAM database authentication for MySQL and PostgreSQL - Amazon Relational Database Service

What are self-signed certificates?

Encrypting Amazon RDS resources - Amazon Relational Database Service

The solution that will improve the performance of the data tier is to deploy an Amazon ElastiCache for Redis cluster in front of the existing DB instance and modify the game to use Redis. This solution will enable the game to store and retrieve the location data of the players in a fast and scalable way, as Redis is an in-memory data store that supports geospatial data types and commands. By using ElastiCache for Redis, the game can reduce the load on the RDS for PostgreSQL DB instance, which is not optimized for high-frequency updates and queries of location data. ElastiCache for Redis also supports replication, sharding, and auto scaling to handle the increasing user base of the game.

The other solutions are not as effective as the first one because they either do not improve the performance, do not support geospatial data, or do not leverage caching. Taking a snapshot of the existing DB instance and restoring it with Multi-AZ enabled will not improve the performance of the data tier, as it only provides high availability and durability, but not scalability or low latency. Migrating from Amazon RDS to Amazon OpenSearch Service with OpenSearch Dashboards will not improve the performance of the data tier, as OpenSearch Service is mainly designed for full-text search and analytics, not for real-time location tracking. OpenSearch Service also does not support geospatial data types and commands natively, unlike Redis. Deploying Amazon DynamoDB Accelerator (DAX) in front of the existing DB instance and modifying the game to use DAX will not improve the performance of the data tier, as DAX is only compatible with DynamoDB, not with RDS for PostgreSQL. DAX also does not support geospatial data types and commands.

References:

Amazon ElastiCache for Redis

Geospatial Data Support - Amazon ElastiCache for Redis

Amazon RDS for PostgreSQL

Amazon OpenSearch Service

Amazon DynamoDB Accelerator (DAX)

This solution meets the following requirements:

It is operationally efficient, as it only requires one transit gateway and one transit VIF to connect the Direct Connect connection to all the VPCs in the same AWS Region. The transit gateway acts as a regional network hub that simplifies the network management and reduces the number of VIFs and gateways needed.

It is scalable, as it can support up to 5000 attachments per transit gateway, which can include VPCs, VPNs, Direct Connect gateways, and peering connections. The transit gateway can also be connected to other transit gateways in different Regions or accounts using peering connections, enabling cross-Region and cross-account connectivity.

It is flexible, as it allows each VPC to communicate with all other VPCs and on-premises networks using dynamic routing protocols such as Border Gateway Protocol (BGP). The transit gateway’s route propagation feature automatically propagates the routes from the attached VPCs and VPNs to the transit gateway route table, eliminating the need to manually update the route tables.

References:

Transit Gateways - Amazon Virtual Private Cloud

Working with transit gateways - AWS Direct Connect

Amazon VPC-to-Amazon VPC connectivity options - Amazon Virtual Private Cloud Connectivity Options

This solution meets the requirements because it allows the company to use both predictive scaling and dynamic scaling to optimize the capacity of its Auto Scaling group. Predictive scaling uses machine learning to analyze historical data and forecast future traffic patterns. It then adjusts the desired capacity of the group in advance of the predicted changes. Dynamic scaling uses target tracking to maintain a specified metric (such as CPU utilization) at a target value. It scales the group in or out as needed to keep the metric close to the target. By using both scaling methods, the company can benefit from faster, simpler, and more accurate scaling that responds to both forecasted and live changes in utilization.

References:

Predictive scaling for Amazon EC2 Auto Scaling

[Target tracking scaling policies for Amazon EC2 Auto Scaling]

Requirement Analysis: The application involves batch processing of large data transfers between EC2 instances.

Data Transfer Costs: Data transfer within the same Availability Zone (AZ) is typically free, while cross-AZ transfers incur additional costs.

Implementation:

Launch all EC2 instances within the same Availability Zone.

Ensure the instances are part of the same subnet to facilitate seamless data transfer.

Conclusion: Placing all EC2 instances in the same AZ reduces data transfer costs significantly without affecting the application’s functionality.

References

AWS Pricing: AWS Data Transfer Pricing

This solution will meet the requirements with the most operational efficiency because:

Amazon Cognito user pools provide a secure and scalable user directory that can store and manage user profiles, and handle user sign-up, sign-in, and access control. User pools can also integrate with social identity providers and enterprise identity providers via SAML or OIDC. User pools can issue JSON Web Tokens (JWTs) that can be used to authenticate users and authorize API requests.

Amazon API Gateway REST APIs enable you to create and deploy APIs that expose your backend services to your clients. REST APIs support multiple authorization mechanisms, including Cognito user pools, IAM, Lambda, and custom authorizers. A Cognito authorizer is a type of Lambda authorizer that uses a Cognito user pool as the identity source. When a client makes a request to a REST API method that is configured with a Cognito authorizer, API Gateway verifies the JWTs that are issued by the user pool and grants access based on the token’s claims and the authorizer’s configuration.

By using Cognito user pools and API Gateway REST APIs with a Cognito authorizer, you can achieve a high level of security, scalability, and performance for your web analytics service. You can also leverage the built-in features of Cognito and API Gateway, such as user management, token validation, caching, throttling, and monitoring, without having to implement them yourself. This reduces the operational overhead and complexity of your solution.

References:

Amazon Cognito User Pools

Amazon API Gateway REST APIs

Use API Gateway Lambda authorizers

Amazon API Gateway with CloudFront: API Gateway allows you to create, deploy, and manage APIs, while CloudFront provides a CDN to deliver content with low latency and high transfer speeds.

AWS WAF (Web Application Firewall):

AWS WAF can be configured in CloudFront to protect against common web exploits, including SQL injection and cross-site scripting (XSS).

WAF allows you to create custom rules to block specific attack patterns and can be managed centrally.

Configuration:

Deploy your APIs using Amazon API Gateway.

Set up an Amazon CloudFront distribution in front of the API Gateway.

Configure AWS WAF on the CloudFront distribution to apply security rules.

Operational Efficiency: This solution provides robust protection with minimal operational overhead by leveraging managed AWS services, ensuring that your APIs are secure without extensive custom implementation.

References:

Using AWS WAF to Protect Your APIs

How CloudFront Works with AWS WAF

A write-through caching strategy adds or updates data in the cache whenever data is written to the database. This ensures that the data in the cache is always consistent with the data in the database. A write-through caching strategy also reduces the cache miss penalty, as data is always available in the cache when it is requested. However, a write-through caching strategy can increase the write latency, as data has to be written to both the cache and the database. A write-through caching strategy is suitable for applications that require high data consistency and low read latency.

A lazy loading caching strategy only loads data into the cache when it is requested, and updates the cache when there is a cache miss. This can result in stale data in the cache, as data is not updated in the cache when it is changed in the database. A lazy loading caching strategy is suitable for applications that can tolerate some data inconsistency and have a low cache miss rate.

An adding TTL caching strategy assigns a time-to-live (TTL) value to each data item in the cache, and removes the data from the cache when the TTL expires. This can help prevent stale data in the cache, as data is periodically refreshed from the database. However, an adding TTL caching strategy can also increase the cache miss rate, as data can be evicted from the cache before it is requested. An adding TTL caching strategy is suitable for applications that have a high cache hit rate and can tolerate some data inconsistency.

An AWS AppConfig caching strategy is not a valid option, as AWS AppConfig is a service that enables customers to quickly deploy validated configurations to applications of any size and scale. AWS AppConfig does not provide a caching layer for web applications.

References: Caching strategies - Amazon ElastiCache, Caching for high-volume workloads with Amazon ElastiCache

Understanding the Requirement: The company needs to automate inventory and updates of diverse OS versions on EC2 instances and summarize common vulnerabilities for monthly reviews.

Analysis of Options:

Systems Manager Patch Manager and Security Hub: Patch Manager automates patching, but Security Hub is more focused on compliance and security posture rather than inventory and vulnerability management.

Systems Manager Patch Manager and Amazon Inspector: Patch Manager automates OS updates, and Amazon Inspector provides vulnerability assessments, making this a comprehensive solution for the requirements.

AWS Shield Advanced and AWS Config: Shield Advanced is for DDoS protection, not suitable for OS patch management and vulnerability reporting.

Amazon GuardDuty and AWS Config: GuardDuty is for threat detection and monitoring, not specifically for patch management and vulnerability assessments.

Best Solution:

Systems Manager Patch Manager and Amazon Inspector: This combination automates OS updates and provides detailed vulnerability assessments, meeting both the inventory and security reporting needs effectively.

References:

AWS Systems Manager Patch Manager

Amazon Inspector

Understanding the Requirement: The company needs to design a scalable and serverless solution for a near-real-time streaming application that experiences high latency due to large amounts of incoming data. The job processing takes about 30 minutes.

Analysis of Options:

Amazon Kinesis Data Firehose: Provides a fully managed service for real-time data streaming and ingestion, allowing for seamless data delivery to destinations such as Amazon S3, Redshift, and Elasticsearch.

AWS Lambda with AWS Step Functions: While suitable for orchestration and lightweight processing, Lambda might not handle long-running jobs (max 15 minutes execution limit) efficiently.

AWS DMS: Primarily used for database migration, not for real-time data ingestion in this context.

Amazon EC2 in Auto Scaling Group: Provides scalability but involves managing servers, which is not serverless and adds operational overhead.

AWS Fargate with ECS: Offers a serverless compute engine for containers, allowing easy scaling and management without managing the underlying infrastructure.

Best Solution:

Amazon Kinesis Data Firehose: For ingesting the streaming data efficiently.

AWS Fargate with ECS: For processing the data in a scalable and serverless manner.

References:

Amazon Kinesis Data Firehose

AWS Fargate

Amazon EFS with Max I/O performance mode is designed for workloads that require high levels of parallelism, such as video processing across multiple EC2 instances. EFS provides shared file storage that can be mounted on both Windows and Linux EC2 instances, and the Max I/O mode ensures the best performance for handling large files and concurrent access across multiple instances.

Option A and B (FSx for Windows File Server): FSx for Windows File Server is optimized for Windows workloads and would not be ideal for Linux instances or high-throughput, parallel workloads.

Option D (EFS General Purpose mode): General Purpose mode offers lower latency but doesn't support the high throughput needed for large, concurrent workloads.

AWS References:

Amazon EFS Performance Modes

The Gateway Load Balancer (GWLB) is specifically designed to route traffic through a security appliance in a hub-and-spoke model, making it the ideal solution for inspecting traffic between multiple AWS accounts. GWLB enables you to simplify, scale, and deploy third-party virtual appliances transparently, and it can work across multiple VPCs or accounts using interface endpoints (Gateway Load Balancer Endpoints).

Key AWS features:

Traffic Inspection: The GWLB allows the centralized security appliance to inspect traffic between different VPCs, making it suitable for inspecting inter-account interactions.

Interface VPC Endpoints: By using interface endpoints in the application accounts, traffic can securely and efficiently be routed to the security appliance in the networking account.

AWS Documentation: The use of GWLB aligns with AWS’s best practices for centralized network security, simplifying architecture and reducing operational complexity?.

Understanding the Requirement: The data science team needs to securely share selective data with the engineering team across multiple AWS accounts with minimal operational overhead.

Analysis of Options:

Copy data to a common account: Involves data duplication and increased storage costs, and requires managing additional permissions.

Lake Formation permissions Grant command: This method can be effective but may involve significant operational overhead if managing permissions across multiple accounts and datasets manually.

AWS Data Exchange: Designed for sharing data externally or between organizations, which adds unnecessary complexity for internal sharing within the same organization.

Lake Formation tag-based access control: Provides a scalable and efficient way to manage access permissions based on tags, allowing fine-grained control and simplified management across accounts.

Best Solution:

Lake Formation tag-based access control: This solution meets the requirements with the least operational overhead, allowing efficient management of cross-account permissions and secure data sharing.

References:

AWS Lake Formation

Tag-based access control

Amazon S3 Intelligent-Tiering: This storage class is designed to optimize costs by automatically moving data between two access tiers (frequent and infrequent) when access patterns change. It provides cost savings without performance impact or operational overhead.

S3 Lifecycle Rules: By creating an S3 Lifecycle rule, the company can automatically transition objects to the Intelligent-Tiering storage class. This eliminates the need for manual intervention and ensures that objects are moved to the most cost-effective storage tier based on their access patterns.

Operational Efficiency: Intelligent-Tiering requires no additional management and delivers immediate availability for frequently accessed objects. This makes it the most operationally efficient solution for the given requirements.

References:

Amazon S3 Intelligent-Tiering

S3 Lifecycle Policies

The most cost-effective solution for the online video game company is to configure a Network Load Balancer with the required protocol and ports for the internet traffic and specify the EC2 instances as the targets. This solution will enable the company to handle millions of UDP requests per second with ultra-low latency and high performance.

A Network Load Balancer is a type of Elastic Load Balancing that operates at the connection level (Layer 4) and routes traffic to targets (EC2 instances, microservices, or containers) within Amazon VPC based on IP protocol data. A Network Load Balancer is ideal for load balancing of both TCP and UDP traffic, as it is capable of handling millions of requests per second while maintaining high throughput at ultra-low latency. A Network Load Balancer also preserves the source IP address of the clients to the back-end applications, which can be useful for logging or security purposes1.

This solution will meet the requirements of minimizing hosting service costs based on demand and providing the streaming content of a movie within 5 minutes of a user purchase. S3 Intelligent-Tiering is a storage class that automatically optimizes storage costs by moving data to the most cost-effective access tier when access patterns change. It is suitable for data with unknown, changing, or unpredictable access patterns, such as newer movies that may have higher demand1. S3 Glacier Flexible Retrieval is a storage class that provides low-cost storage for archive data that is retrieved asynchronously. It offers flexible data retrieval options from minutes to hours, and free bulk retrievals in 5-12 hours. It is ideal for backup, disaster recovery, and offsite data storage needs2. By using expedited retrieval, the user can access the older movie video file in 1-5 minutes, which meets the requirement of 5 minutes3.

References: 1: Amazon S3 Intelligent-Tiering Storage Class | AWS4, Overview section2: Amazon S3 Glacier Flexible Retrieval and Glacier Deep Archive Retrieval …1, Amazon S3 Glacier Flexible Retrieval section3: Amazon S3 Glacier Flexible Retrieval and Glacier Deep Archive Retrieval …1, Retrieval Rates section.

Understanding the Requirement: The company needs a disaster recovery solution for FSx for NetApp ONTAP in a secondary region, accessible using the same protocols (CIFS and NFS).

Analysis of Options:

Lambda Function and S3: Involves copying data to S3, which changes the access protocols and increases operational overhead.

AWS Backup: Suitable for backup and restore but not for real-time or near-real-time replication for disaster recovery.

FSx for ONTAP with SnapMirror: SnapMirror provides efficient replication between ONTAP instances, maintaining access protocols and requiring minimal operational overhead.

Amazon EFS: Does not support CIFS and requires migrating data, increasing complexity and changing access protocols.

Best Solution:

FSx for ONTAP with SnapMirror: This solution ensures seamless disaster recovery with the same access protocols and minimal operational overhead.

References:

Amazon FSx for NetApp ONTAP

NetApp SnapMirror

AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy to prepare and load your data for analytics. AWS Glue can automatically discover your data in Amazon S3 and catalog it, so you can query and search the data using SQL. AWS Glue can also run serverless ETL jobs using Apache Spark and Python to transform and load your data into various destinations, such as Amazon Redshift, Amazon Athena, or Amazon Aurora. AWS Glue is a serverless service, so you only pay for the resources consumed by the jobs, and you don’t need to provision or manage any infrastructure.

Amazon Redshift is a fully managed, petabyte-scale data warehouse service that enables you to use standard SQL and your existing business intelligence (BI) tools to analyze your data. Amazon Redshift also supports massively parallel processing (MPP), which means it can distribute and execute queries across multiple nodes in parallel, delivering fast performance and scalability. Amazon Redshift Serverless is a new option that automatically scales query compute capacity based on the queries being run, so you don’t need to manage clusters or capacity. You only pay for the query processing time and the storage consumed by your data.

Amazon Redshift ML is a feature that enables you to create, train, and deploy machine learning (ML) models using familiar SQL commands. Amazon Redshift ML can automatically discover the best model and hyperparameters for your data, and store the model in Amazon SageMaker, a fully managed service that provides a comprehensive set of tools for building, training, and deploying ML models. You can then use SQL functions to apply the model to your data in Amazon Redshift and generate predictions.

The combination of AWS Glue, Amazon Redshift Serverless, and Amazon Redshift ML meets the requirements of the question, as it provides a serverless, scalable, and SQL-based solution to transform, load, and analyze the data from the Amazon S3 data lake, and to create and train ML models on the data.

Option A is not correct, because Amazon EMR is not a serverless service. Amazon EMR is a managed service that simplifies running Apache Spark, Apache Hadoop, and other big data frameworks on AWS. Amazon EMR requires you to launch and configure clusters of EC2 instances to run your ETL jobs, which adds complexity and cost compared to AWS Glue.

Option B is not correct, because Amazon Aurora Serverless is not a data warehouse service, and it does not support MPP. Amazon Aurora Serverless is an on-demand, auto-scaling configuration for Amazon Aurora, a relational database service that is compatible with MySQL and PostgreSQL. Amazon Aurora Serverless can automatically adjust the database capacity based on the traffic, but it does not distribute the data and queries across multiple nodes like Amazon Redshift does. Amazon Aurora Serverless is more suitable for transactional workloads than analytical workloads.

Option D is not correct, because Amazon Athena is not a data warehouse service, and it does not support MPP. Amazon Athena is an interactive query service that enables you to analyze data in Amazon S3 using standard SQL. Amazon Athena is serverless, so you only pay for the queries you run, and you don’t need to load the data into a database. However, Amazon Athena does not store the data in a columnar format, compress the data, or optimize the query execution plan like Amazon Redshift does. Amazon Athena is more suitable for ad-hoc queries than complex analytics and ML.

References:

AWS Glue

Amazon Redshift

Amazon Redshift Serverless

Amazon Redshift ML

Amazon EMR

Amazon Aurora Serverless

Amazon Athena

Amazon S3 Intelligent-Tiering is the most cost-effective solution for this scenario, providing both high availability and durability while adjusting automatically to changing access patterns. It moves data across two access tiers: one optimized for frequent access and another for infrequent access, based on usage patterns. This tiering ensures that the company avoids paying for unused storage while also keeping frequently accessed data in a more accessible tier.

Key AWS references and benefits of S3 Intelligent-Tiering:

High Durability and Availability: Amazon S3 offers 99.999999999% durability and 99.9% availability for objects stored, ensuring data is always protected.

Automatic Tiering: Data is automatically moved between tiers based on access patterns, making it ideal for workloads with unpredictable or variable access patterns.

No Retrieval Fees: Unlike S3 One Zone-IA or Glacier, there are no retrieval fees, making this more cost-effective in scenarios where access patterns vary over time.

AWS Documentation: According to the AWS Well-Architected Framework under the Cost Optimization Pillar, S3 Intelligent-Tiering is recommended for storage when access patterns change over time, as it minimizes costs while maintaining availability?.

S3 One Zone-IA:

Suitable for infrequently accessed data that doesn't require multiple Availability Zone resilience.

Cost-effective for storing user-uploaded images that are only used for AI model training twice a year.

S3 Standard:

Ideal for frequently accessed data with high durability and availability.

Store premium user-generated AI images here to ensure they are readily available for download at any time.

S3 Standard-IA:

Cost-effective storage for data that is accessed less frequently but still requires rapid retrieval.

Store non-premium user-generated AI images here, as these images are only downloaded once every 6 hours, making it a good balance between cost and accessibility.

Cost-Effectiveness: This solution optimizes storage costs by categorizing data based on access patterns and durability requirements, ensuring that each type of data is stored in the most cost-effective manner.

References:

Amazon S3 Storage Classes

S3 One Zone-IA

Amazon Textract can extract text from the PDFs, and Amazon Comprehend is the most suitable service to analyze the extracted text for sentiment and insights. Comprehend offers a fully managed, low-operational overhead solution for analyzing text data. The results can then be stored in an Amazon S3 bucket, ensuring scalability and easy access.

Option A: Athena is for querying structured data and is not suitable for sentiment analysis.

Option B: SageMaker adds complexity and is not necessary when Comprehend can handle sentiment analysis natively.

Option D: QuickSight is used for visualization and analytics, but it does not provide sentiment analysis.

AWS References:

Amazon Comprehend

Amazon Textract

Understanding the Requirement: The company wants to migrate an application to AWS, increase its availability, and use AWS WAF in the architecture.

Analysis of Options:

Auto Scaling group with ALB and WAF: This option provides high availability by distributing instances across multiple Availability Zones. The ALB ensures even traffic distribution, and AWS WAF provides security at the application layer.

Cluster placement group with ALB and WAF: Cluster placement groups are for low-latency networking within a single AZ, which does not provide the high availability across AZs.

Two EC2 instances with ALB and WAF: This setup provides some availability but does not scale automatically, missing the benefits of an Auto Scaling group.

Auto Scaling group with WAF directly: AWS WAF cannot be directly connected to an Auto Scaling group; it needs to be attached to an ALB, CloudFront distribution, or API Gateway.

Best Solution:

Auto Scaling group with ALB and WAF: This configuration ensures high availability, scalability, and security, meeting all the requirements effectively.

References:

Amazon EC2 Auto Scaling

Application Load Balancer

AWS WAF

https://aws.amazon.com/premiumsupport/knowledge-center/dynamodb-high-latency/

Amazon DynamoDB Accelerator (DAX) is a fully managed in-memory cache for DynamoDB that improves the performance of DynamoDB tables by up to 10 times and provides microsecond level of response time at any scale. It is compatible with DynamoDB API operations and requires minimal code changes to use1. By configuring DAX for the new messages table, the solution can reduce the latency for reading new messages with minimal application changes.

B. Add DynamoDB read repticas to handle the increased read load. Update the application to point to the read endpoint for the read replicas. This solution will not work, as DynamoDB does not support read replicas as a feature. Read replicas are available for Amazon RDS, not for DynamoDB2.

C. Double the number of read capacity units for the new messages table in DynamoDB. Continue to use the existing DynamoDB endpoint. This solution will not meet the requirement of reading new messages with as little latency as possible, as increasing the read capacity units will only increase the throughput of DynamoDB, not the performance or latency3.

D. Add an Amazon ElastiCache for Redis cache to the application stack. Update the application to point to the Redis cache endpoint instead of DynamoDB. This solution will not meet the requirement of minimal application changes, as adding ElastiCache for Redis will require significant code changes to implement caching logic, such as querying cache first, updating cache after writing to DynamoDB, and invalidating cache when needed.

Reference URL: https://aws.amazon.com/dynamodb/dax/

AWS DataSync is a service that makes it easy to move large amounts of data between AWS storage services and on-premises storage systems. AWS DataSync can copy files from an S3 bucket to an EFS file system and another S3 bucket continuously, as well as overwrite only the files that have changed in the source. This solution will meet the requirements with the least operational overhead, as it does not require any code development or manual intervention.

References:

4 explains how to create AWS DataSync locations for different storage services.

5 describes how to create and configure AWS DataSync tasks for data transfer.

6 discusses the different transfer modes that AWS DataSync supports.

Amazon Aurora Serverless is a cost-effective, on-demand, autoscaling configuration for Amazon Aurora. It automatically adjusts the database's capacity based on the current demand, which is ideal for workloads with variable and unpredictable usage patterns. Since the application is expected to be read-heavy with occasional writes and steady growth, Aurora Serverless can provide the necessary performance without requiring the management of database instances.

Cost-Optimization: Aurora Serverless only charges for the database capacity you use, making it a more cost-effective solution compared to always running provisioned database instances, especially for workloads with fluctuating demand.

Scalability: It automatically scales database capacity up or down based on actual usage, ensuring that you always have the right amount of resources available.

Performance: Aurora Serverless is built on the same underlying storage as Amazon Aurora, providing high performance and availability.

Why Not Other Options?:

Option A (RDS with Provisioned IOPS SSD): While Provisioned IOPS SSD ensures consistent performance, it is generally more expensive and less flexible compared to the autoscaling nature of Aurora Serverless.

Option C (DynamoDB with On-Demand Capacity): DynamoDB is a NoSQL database and may not be the best fit for applications requiring relational database features.

Option D (RDS with Magnetic Storage and Read Replicas): Magnetic storage is outdated and generally slower. While read replicas help with read-heavy workloads, the overall performance might not be optimal, and magnetic storage doesn’t provide the necessary performance.

AWS References:

Amazon Aurora Serverless - Information on how Aurora Serverless works and its use cases.

Amazon Aurora Pricing - Details on the cost-effectiveness of Aurora Serverless.

This answer is correct because it meets the requirements of migrating a 20 TB MySQL database within 2 weeks with minimal downtime and cost-effectively. The AWS Snowball Edge Storage Optimized device has up to 80 TB of usable storage space, which is enough to fit the database. The AWS Database Migration Service (AWS DMS) can migrate data from MySQL to Amazon Aurora, Amazon RDS for MySQL, or MySQL on Amazon EC2 with minimal downtime by continuously replicating changes from the source to the target. The AWS Schema Conversion Tool (AWS SCT) can convert the source schema and code to a format compatible with the target database. By using these services together, the company can migrate the database to AWS with minimal downtime and cost. The Snowball Edge device can be shipped back to AWS to finish the migration and continue the ongoing replication until the database is fully migrated.

References:

https://docs.aws.amazon.com/snowball/latest/developer-guide/device-differences.html

https://docs.aws.amazon.com/dms/latest/userguide/CHAP_Source.MySQL.html

https://docs.aws.amazon.com/SchemaConversionTool/latest/userguide/CHAP_Source.MySQL.htm

EBS Encryption:

Default EBS Encryption: Can be enabled for new EBS volumes.

Use of AWS KMS: Specify AWS KMS keys to handle encryption and decryption of data transparently.

Amazon RDS Encryption:

RDS Encryption: Encrypts the underlying storage for RDS instances using AWS KMS.

Configuration: Enable encryption when creating the RDS instance or modify an existing instance to enable encryption.

Least Amount of Changes:

Both EBS and RDS support seamless encryption with AWS KMS, requiring minimal changes to the existing infrastructure.

Enables compliance with regulatory requirements without modifying the application.

Operational Efficiency: Using AWS KMS for both EBS and RDS ensures a consistent, managed approach to encryption, simplifying key management and enhancing security.

References:

Amazon EBS Encryption

Amazon RDS Encryption

AWS Key Management Service

Requirement Analysis: The application was overwhelmed with unexpected data volume, leading to data loss and the need for a replay mechanism.

Amazon SQS Overview: SQS is a fully managed message queuing service that decouples and scales microservices, distributed systems, and serverless applications.

Data Decoupling: By using an SQS queue, the application can store incoming tracker data reliably and process it asynchronously, preventing data loss.

Implementation:

Create an SQS queue.

Modify the web application to send incoming data to the SQS queue.

Configure the application instances to poll the SQS queue and process the messages.

Conclusion: This solution meets the requirements with minimal operational overhead, ensuring data is not lost and can be processed at the application’s own pace.

References

Amazon SQS: Amazon SQS Documentation

When designing a microservice architecture where each microservice interacts with different AWS services, it's essential to follow the principle of least privilege. This means granting each microservice only the permissions it needs to perform its tasks, reducing the risk of unauthorized access or accidental actions.

The recommended approach is to create individual IAM roles with policies that grant each microservice the specific permissions it requires. Then, these roles should be associated with the EC2 instances that run the corresponding microservice. By doing so, each EC2 instance will assume its specific IAM role, and permissions will be automatically managed by AWS.

IAM roles provide temporary credentials via the instance metadata service, eliminating the need to hard-code credentials in your application code, which enhances security.

AWS References:

IAM Roles for Amazon EC2 explains how EC2 instances can use IAM roles to securely access AWS services without managing long-term credentials.

Best Practices for IAM includes recommendations for implementing the least privilege principle and using IAM roles effectively.

Why the other options are incorrect:

A. Assign an IAM user to each microservice: This requires managing long-term credentials (access keys), which should be avoided. Storing keys in application code is insecure and creates a maintenance burden.

B. Create a single IAM role: This violates the principle of least privilege, as a single role with broad permissions across all services is less secure.

C. Use AWS Organizations: This approach adds unnecessary complexity. Managing permissions at the account level for each microservice is excessive for this use case and doesn't adhere to the principle of least privilege.

Understanding the Requirement: The company needs to transfer 50 TB of data to AWS with minimal operational overhead and no available network bandwidth for the transfer. The transformation job must continue running in the AWS Cloud.

Analysis of Options:

AWS DataSync and AWS Glue: DataSync is suitable for online data transfer, but there is no available network bandwidth. AWS Glue can be used for data transformation but does not solve the bandwidth issue.

AWS Snowcone: Snowcone is a smaller device suitable for smaller data transfers, and deploying the transformation application on it may not be feasible for 50 TB of data.

AWS Snowball Edge Storage Optimized with Glue: This device is designed for large data transfers. Copying the data to the device is straightforward, and AWS Glue can handle data transformation in the cloud.

AWS Snowball Edge Storage Optimized with EC2: This involves setting up EC2 instances for transformation, adding operational complexity compared to using AWS Glue.

Best Solution:

AWS Snowball Edge Storage Optimized with AWS Glue: This provides the least operational overhead for transferring large amounts of data and setting up the transformation job in the cloud.

References:

AWS Snowball Edge

AWS Glue

AWS Lambda for Operational Efficiency:

AWS Lambda is a serverless compute service that allows you to run code without provisioning or managing servers. It automatically scales based on the number of invocations and eliminates the need to maintain and monitor EC2 instances, making it far more operationally efficient compared to running a cron job on EC2.

By using Lambda, you pay only for the compute time that your function uses. This is especially beneficial when dealing with lightweight tasks, such as scanning a DynamoDB table and sending email messages once a day.

Amazon DynamoDB:

DynamoDB is a highly scalable, fully managed NoSQL database. The table stores employee start dates, and scanning the table to find the employees who have a work anniversary on the current day is a lightweight operation. Lambda can easily perform this operation using the DynamoDB Scan API or queries, depending on how the data is structured.

Amazon SNS for Email Notifications:

Amazon Simple Notification Service (SNS) is a fully managed messaging service that supports sending notifications to a variety of endpoints, including email. SNS is well-suited for sending out email messages to employees, as it can handle the fan-out messaging pattern (sending the same message to multiple recipients).

In this scenario, once Lambda identifies employees who have their work anniversaries, it can use SNS to send the email notifications efficiently. SNS integrates seamlessly with Lambda, and sending emails via SNS is a common pattern for this type of use case.

Event Scheduling:

To automate this daily task, you can schedule the Lambda function using Amazon EventBridge (formerly CloudWatch Events). EventBridge can trigger the Lambda function on a daily schedule (cron-like scheduling). This avoids the complexity and operational overhead of manually setting up cron jobs on EC2 instances.

Why Not EC2 or SQS?:

Option A & B suggest running a cron job on an Amazon EC2 instance. This approach requires you to manage, scale, and patch the EC2 instance, which increases operational overhead. Lambda is a better choice because it automatically scales and doesn’t require server management.

Amazon Simple Queue Service (SQS) is ideal for decoupling distributed systems but isn’t necessary in this context because the goal is to send notifications to employees on their work anniversaries. SQS adds unnecessary complexity for this straightforward use case, where SNS is the simpler and more efficient solution.

AWS References:

AWS Lambda

Amazon SNS

Amazon DynamoDB

Amazon EventBridge

Summary:

Using AWS Lambda combined with Amazon SNS to send notifications, and scheduling the function with Amazon EventBridge to run daily, is the most operationally efficient solution. It leverages AWS serverless technologies, which reduce the need for infrastructure management and provide automatic scaling. Therefore, Option C is the correct and optimal choice.

Understanding the Requirement: The company wants to optimize costs and reduce operational overhead for an RDS for PostgreSQL database that only needs to be active during business hours on weekdays.

Analysis of Options:

Instance Scheduler on AWS: Allows for automated start and stop schedules based on specified times, ideal for resources only needed during certain hours. This directly optimizes costs by running the database only when needed.

Turn off automatic backups and create weekly snapshots: Does not address the requirement of reducing operational overhead and optimizing runtime costs.

Custom Lambda function: This could work but adds unnecessary complexity compared to using the Instance Scheduler.

All Upfront Reserved DB Instances: While this reduces costs, it does not optimize for usage patterns that require the database only during specific hours.

Best Solution:

Instance Scheduler on AWS: This option effectively manages the database runtime based on the specified schedule, reducing costs and operational overhead.

References:

Instance Scheduler on AWS

To process each form exactly once and ensure no data is lost, using an Amazon SQS FIFO (First-In-First-Out) queue is the most appropriate solution. SQS FIFO queues guarantee that messages are processed in the exact order they are sent and ensure that each message is processed exactly once. This ensures data consistency and reliability, both of which are crucial for processing user-submitted forms without data loss.

SQS acts as a buffer between the web application server and the worker tier, ensuring that submitted forms are stored reliably and forwarded to the worker tier for processing. This also decouples the application, improving its scalability and resilience.

Option B (API Gateway): API Gateway is better suited for API management rather than acting as a message queue for form processing.

Option C (SQS Standard Queue): While SQS Standard queues offer high throughput, they do not guarantee exactly-once processing or the strict ordering needed for this use case.

Option D (Step Functions): Step Functions are useful for orchestrating workflows but add unnecessary complexity for simple message queuing and form processing.

AWS References:

Amazon SQS FIFO Queues

Decoupling Application Tiers Using Amazon SQS

Requirement Analysis: The goal is to migrate from Microsoft SQL Server to Amazon Aurora PostgreSQL with minimal application code changes.

Babelfish for Aurora PostgreSQL: Babelfish allows Aurora PostgreSQL to understand SQL Server queries natively, reducing the need for application code changes.

AWS Schema Conversion Tool (SCT): This tool helps in converting the database schema from SQL Server to PostgreSQL.

AWS Database Migration Service (DMS): DMS can be used to migrate data from SQL Server to Aurora PostgreSQL seamlessly.

Combined Approach: Enabling Babelfish addresses the SQL query compatibility, while SCT and DMS handle the schema and data migration.

References

Babelfish for Aurora PostgreSQL: Babelfish Documentation

AWS SCT and DMS: AWS Database Migration Service

To address the issue of bandwidth limitations on the company's on-premises application, and to minimize the impact on internal user connectivity, a new AWS Direct Connect connection should be established to direct backup traffic through this new connection. This solution will offer a secure, high-speed connection between the company's data center and AWS, which will allow the company to transfer data quickly without consuming internet bandwidth.

"Security groups create an outbound rule for every inbound rule." Not completely right. Statefull does NOT mean that if you create an inbound (or outbound) rule, it will create an outbound (or inbound) rule. What it does mean is: suppose you create an inbound rule on port 443 for the X ip. When a request enters on port 443 from X ip, it will allow traffic out for that request in the port 443. However, if you look at the outbound rules, there will not be any outbound rule on port 443 unless explicitly create it. In ACLs, which are stateless, you would have to create an inbound rule to allow incoming requests and an outbound rule to allow your application responds to those incoming requests.

https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html#SecurityGroupRules

AWS Config is a service that enables you to assess, audit, and evaluate the configurations of your AWS resources. By creating a Config rule, you can automatically check whether your Amazon EBS volumes are encrypted and flag those that are not, with minimal cost and configuration effort.

AWS Config Rule: AWS Config provides managed rules that you can use to automatically check the compliance of your resources against predefined or custom criteria. In this case, you would create a rule to evaluate EBS volumes and determine if they are encrypted. If a volume is not encrypted, the rule will flag it, allowing you to take corrective action.

Operational Overhead: This approach significantly reduces operational overhead because once the rule is in place, it continuously monitors your EBS volumes for compliance, and there’s no need for manual checks or custom scripting.

Why Not Other Options?:

Option A (Lambda with API calls and EventBridge): While this can work, it involves writing and maintaining custom code, which increases operational overhead compared to using a managed AWS Config rule.

Option B (API calls on Fargate): Running API calls on Fargate is more complex and costly compared to using AWS Config, which provides a simpler, managed solution.

Option C (IAM policy with Cost Explorer): This option does not directly enforce encryption compliance and involves manual intervention, making it less efficient and more prone to errors.

AWS References:

AWS Config Rules - Overview of AWS Config rules and how they can be used to evaluate resource configurations.

Amazon EBS Encryption - Information on how to manage and enforce encryption for EBS volumes.

Gateway Endpoint for Amazon S3: A VPC endpoint for Amazon S3 allows you to privately connect your VPC to Amazon S3 without requiring an internet gateway, NAT device, VPN connection, or AWS Direct Connect connection.

Provisioning the Endpoint:

Navigate to the VPC Dashboard.

Select "Endpoints" and create a new endpoint.

Choose the service name for S3 (com.amazonaws.region.s3).

Select the appropriate VPC and subnets.

Adjust the route tables of the subnets to include the new endpoint.

Update Route Tables: Modify the route tables of the subnets to direct traffic destined for S3 to the newly created endpoint. This ensures that traffic to S3 does not go through the NAT instance, avoiding the saturated network and eliminating timeouts.

Operational Efficiency: This solution minimizes operational overhead by removing dependency on the NAT instance and avoiding internet traffic, leading to more stable and secure S3 interactions.

References:

VPC Endpoints for Amazon S3

Creating a Gateway Endpoint

Understanding the Requirement: The development team needs to prevent the launch of large EC2 instances across multiple AWS accounts used for development, staging, and production environments.

Analysis of Options:

IAM Policies: Would need to be applied individually to each user in every account, leading to significant operational overhead.

AWS Resource Access Manager: Used for sharing resources, not for enforcing restrictions on resource creation.

IAM Role in Each Account: Requires creating and managing roles in each account, leading to higher operational overhead compared to using a centralized approach.

Service Control Policy (SCP) with AWS Organizations: Provides a centralized way to enforce policies across multiple AWS accounts, ensuring that large EC2 instances cannot be launched in any account.

Best Solution:

Service Control Policy (SCP) with AWS Organizations: This solution offers the least operational overhead by allowing centralized management and enforcement of policies across all accounts, effectively preventing the launch of large EC2 instances.

References:

AWS Organizations and SCPs

To address the high CPU utilization on the EC2 instance and the degraded performance of Amazon RDS for read requests, the solution involves two key actions: resizing the EC2 instance and leveraging Amazon RDS read replicas.

Resizing the EC2 Instance: The first step is to resize the EC2 instance to a type with more CPU capacity to handle the higher computational demands during peak usage times. This helps to alleviate the immediate pressure on the CPU.

Auto Scaling Group with a Size of 1: Although the application can only run on a single EC2 instance due to its monolithic nature, creating an Auto Scaling group with a minimum and maximum size of 1 ensures that the instance is automatically restarted or replaced in case of failure, maintaining high availability.

RDS Read Replica: Configuring an RDS read replica allows the application to offload read requests to a separate instance, thus reducing the load on the primary RDS instance. This improves the performance of read operations, which were previously bottlenecked due to the high CPU usage on the EC2 instance.

Why Not Other Options?:

Option B: Redirecting all traffic to the RDS read replica is not recommended because replicas are meant for read traffic only, not for write operations. This could lead to data consistency issues.

Option C: Increasing the RDS instance type capacity helps, but it doesn’t address the high CPU usage on the EC2 instance, nor does it provide a solution for scaling reads.

Option D: While resizing both the EC2 and RDS instances increases their capacities, it doesn’t address the specific need to offload read traffic from the primary RDS instance.

AWS References:

Amazon RDS Read Replicas - Explains how to create and use read replicas to offload read traffic from the primary database instance.

Resizing Your EC2 Instance - Guidance on resizing EC2 instances to meet workload demands.

This solution provides the most secure access for external support engineers with the least exposure to potential security risks.

AWS Systems Manager (SSM) and Session Manager: Systems Manager Session Manager allows secure and auditable access to EC2 instances without the need to open inbound SSH ports or manage SSH keys. This reduces the attack surface significantly. The SSM Agent must be installed and configured on all instances, and the instances must have an instance profile with the necessary IAM permissions to connect to Systems Manager.

IAM Identity Center: IAM Identity Center provides centralized management of access to the AWS Management Console for external support engineers. By using IAM Identity Center, you can control console access securely and ensure that external engineers have the appropriate permissions based on their roles.

Why Not Other Options?:

Option B (Local IAM user credentials): This approach is less secure because it involves managing local IAM user credentials and does not leverage the centralized management and security benefits of IAM Identity Center.

Option C (Security group with SSH access): Allowing SSH access opens up the infrastructure to potential security risks, even when restricted by IP addresses. It also requires managing SSH keys, which can be cumbersome and less secure.

Option D (Bastion host): While a bastion host can secure SSH access, it still requires managing SSH keys and opening ports. This approach is less secure and more operationally intensive compared to using Session Manager.

AWS References:

AWS Systems Manager Session Manager - Documentation on using Session Manager for secure instance access.

AWS IAM Identity Center - Overview of IAM Identity Center and its capabilities for managing user access.

Understanding the Requirement: The company wants to restrict each application to its specific prefix in an S3 bucket and have granular control over the objects under each prefix.

Analysis of Options:

Dedicated S3 Access Points: Provides a scalable and flexible way to manage access to S3 buckets, allowing specific policies to be attached to each access point, thereby controlling access at the prefix level.

S3 Batch Operations: Suitable for large-scale changes but involves more operational overhead and does not dynamically control future access.

Replication to new S3 buckets: Involves unnecessary duplication of data and increased storage costs, and operational overhead for managing multiple buckets.

Combination of replication and access points: Adds unnecessary complexity and overhead compared to using access points directly.

Best Solution:

Dedicated S3 Access Points: This provides the least operational overhead while meeting the requirements for prefix-level access control and granular management.

References:

Amazon S3 Access Points

This solution is the most cost-effective and efficient way to break down costs per application.

Tagging Resources: By tagging all AWS resources with a specific key (e.g., "cost") and a value representing the application's name, you can easily identify and categorize costs associated with each application. This tagging strategy allows for granular tracking of costs within AWS.

Activating Cost Allocation Tags: Once tags are applied to resources, you need to activate cost allocation tags in the AWS Billing and Cost Management console. This ensures that the costs associated with each tag are included in your billing reports and can be used for cost analysis.

AWS Cost Explorer: Cost Explorer is a powerful tool that allows you to visualize, understand, and manage your AWS costs and usage over time. You can filter and group your cost data by the tags you’ve applied to resources, enabling you to easily see the cost breakdown for each application. Cost Explorer also supports generating regular reports, which can be scheduled and emailed to stakeholders.

Why Not Other Options?:

Option A (AWS Budgets): AWS Budgets is more focused on setting cost and usage thresholds and monitoring them, rather than providing detailed cost breakdowns by application.

Option B (Load Cost and Usage Reports into RDS): This approach is less cost-effective and involves more operational overhead, as it requires setting up and maintaining an RDS instance and running SQL queries.

Option D (AWS Billing and Cost Management Console): While you can download bills, this method is more manual and less dynamic compared to using Cost Explorer with activated tags.

AWS References:

AWS Tagging Strategies - Overview of how to use tagging to organize and track AWS resources.

AWS Cost Explorer - Details on how to use Cost Explorer to analyze costs.

Amazon Elastic File System (EFS) is a managed file storage service that can be mounted across multiple EC2 instances. It provides a scalable and high-performing solution to share data among instances within a VPC.

High Performance: EFS provides scalable performance for workloads that require high throughput and IOPS. It is particularly well-suited for applications that need to share data across multiple instances.

Ease of Use: EFS can be easily mounted on multiple instances across different Availability Zones, providing a shared file system accessible to all the instances within the VPC.

Security: EFS can be configured to ensure that data remains within the VPC, and it supports encryption at rest and in transit.

Why Not Other Options?:

Option A (Amazon S3 bucket with APIs): While S3 is excellent for object storage, it is not a file system and does not provide the low-latency access required for shared data between instances.

Option B (S3 bucket as a mounted volume): S3 is not designed to be mounted as a file system, and this approach would introduce unnecessary complexity and latency.

Option C (EBS volume shared across instances): EBS volumes cannot be attached to multiple instances simultaneously. It is not designed to be shared across instances like EFS.

AWS References:

Amazon EFS - Overview of Amazon EFS and its features.

Best Practices for Amazon EFS - Recommendations for using EFS with multiple instances.

An S3 Bucket Key reduces the cost of using AWS KMS for server-side encryption by decreasing the number of requests made to KMS. By enabling S3 Bucket Key, the company can meet its encryption requirements with KMS keys while optimizing costs by reducing the number of KMS API requests.

Key AWS features:

Cost Optimization: S3 Bucket Keys reduce the frequency of KMS calls, optimizing the cost associated with encryption while still using AWS KMS for key management.

Compliance with KMS Encryption: This solution continues to meet the strict encryption requirements of the company by using KMS managed keys.

AWS Documentation: Using an S3 Bucket Key is recommended for organizations looking to optimize encryption costs without compromising security??.

For an application requiring TCP communication and high availability:

Network Load Balancer (NLB) is the best choice for load balancing TCP traffic because it is designed for handling high-throughput, low-latency connections.

Auto Scaling group ensures that the application can automatically scale based on demand, adding or removing EC2 instances as needed, which is crucial for handling user growth.

Option C (Application Load Balancer): ALB is primarily for HTTP/HTTPS traffic, not ideal for TCP.

Option D (Manual scaling): Manually adding instances does not provide the automation or scalability required.

Option E (Gateway Load Balancer): GLB is used for third-party virtual appliances, not for direct application load balancing.

AWS References:

Network Load Balancer

Auto Scaling Group

Understanding the Requirement: The company wants to maximize cost savings for their application over the next three years, with the flexibility to change the instance family and sizes within the next six months based on application popularity and usage.

Analysis of Options:

Compute Savings Plan: This plan offers the most flexibility, allowing the company to change instance families, sizes, and regions. It applies to EC2, AWS Fargate, and AWS Lambda, offering significant cost savings with this flexibility.

EC2 Instance Savings Plan: This plan is less flexible than the Compute Savings Plan, as it only applies to EC2 instances and allows changes within a specific instance family.

Zonal Reserved Instances: These provide a discount on EC2 instances but are tied to a specific availability zone and instance type, offering the least flexibility.

Standard Reserved Instances: These offer discounts on EC2 instances but with more restrictions compared to Savings Plans, particularly when changing instance types and families.

Best Option for Flexibility and Savings:

The Compute Savings Plan is the most cost-effective solution because it allows the company to maintain flexibility while still achieving significant cost savings. This is critical for adapting to changing application demands without being locked into specific instance types or families.

References:

AWS Savings Plans

EC2 Instance Types

The most cost-effective solution for serving video content with different bitrates is to store multiple versions of each video in Amazon S3. S3 provides scalable and cost-effective storage for large media files. Serving the videos from a single Amazon EC2 instance ensures low-latency delivery, and S3 storage helps minimize costs.

Option A (ElastiCache): Caching large video files in memory would be prohibitively expensive and unnecessary.

Option B (Auto Scaling group): Using Auto Scaling groups to serve video is less cost-effective compared to leveraging S3 for static storage.

Option D (Kinesis Video Streams): Kinesis Video Streams is designed for real-time video streaming and is not suitable for storing and serving pre-recorded videos.

AWS References:

Amazon S3 for Media Storage

AWS Direct Connect: Provides a dedicated network connection from your on-premises data center to AWS, ensuring low latency and consistent network performance.

Direct Connect Gateway Association:

Direct Connect Gateway: Acts as a global network transit hub to connect VPCs across different AWS regions.

Association with Transit Gateway: Enables communication between on-premises data centers and multiple VPCs connected to the transit gateway.

Transit Virtual Interface (VIF):

Create Transit VIF: To connect Direct Connect with a transit gateway.

Setup Steps:

Establish a Direct Connect connection.

Create a transit VIF to the Direct Connect gateway.

Associate the Direct Connect gateway with the transit gateway attached to the VPCs.

Cost Efficiency: This combination avoids the recurring costs and potential performance variability of VPN connections, providing a robust, low-latency hybrid network solution.

References:

AWS Direct Connect

Transit Gateway and Direct Connect Gateway

Migrate the database to Amazon Aurora MySQL - this will let the DB scale on it's own; it'll scale automatically without needing adjustment. Create AMI of the web app and using a launch template - this will make the creating of any future instances of the app seamless. They can then be added to the auto scaling group which will save them money as it will scale up and down based on demand. Using a spot fleet to launch instances- This solves the "MOST cost-effective" portion of the question as spot instances come at a huge discount at the cost of being terminated at any time Amazon deems fit. I think this is why there's a bit of disagreement on this. While it's the most cost effective, it would be a terrible choice if amazon were to terminate that spot instance during a busy period.

https://docs.aws.amazon.com/whitepapers/latest/aws-vpc-connectivity-options/aws-direct-connect-aws-transit-gateway.html

Amazon Elastic File System (EFS) is the ideal solution for migrating legacy applications that require NFS (Network File System) communication. EFS provides fully managed, scalable NFS storage in the cloud, and it supports the standard NFS protocols, allowing the legacy application to continue using NFS without modification after migration to AWS.

Key AWS features:

NFS Support: EFS natively supports the NFSv4 protocol, which makes it the best solution for workloads that rely on NFS communication.

Scalability and Availability: EFS automatically scales as application demands grow, making it a highly available and reliable storage solution.

AWS Documentation: According to AWS’s best practices for file storage, EFS is recommended for any workloads requiring NFS support in a cloud environment??.

Amazon CloudFront is a content delivery network (CDN) service that caches copies of your content at edge locations around the world. This helps improve performance by serving content from the edge nearest to the user. However, when the content in Amazon S3 (your origin) is updated, those updates may not immediately reflect on the website if they are cached at the CloudFront edge locations.

The issue described in the question suggests that the CI/CD pipeline is functioning correctly, and updates are being deployed to S3. However, since CloudFront caches this content, the edge locations may still be serving outdated content, causing the updates to not be reflected on the website.

To resolve this issue, you need to invalidate the CloudFront cache. By invalidating the cache, CloudFront will remove the outdated content and retrieve the latest version from the S3 origin.

AWS documentation on this process:

CloudFront cache invalidation allows you to clear items from the cache so that CloudFront retrieves the latest version from the origin. You can create invalidation requests via the AWS Management Console, AWS CLI, or SDKs.

AWS CloudFront Documentation

Why the other options are incorrect:

A. Add an Application Load Balancer: ALBs are used to distribute incoming application traffic and are not relevant to caching or serving content from CloudFront.

B. Add Amazon ElastiCache for Redis or Memcached: This would help in caching database queries but has no relation to static website content hosted on CloudFront and S3.

D. Use AWS Certificate Manager (ACM): ACM is used for managing SSL/TLS certificates and is unrelated to the issue of content not being updated on CloudFront.

For cost-effectiveness and high availability in Amazon EMR workloads, the best approach is to configure a transient cluster (which runs for the duration of the job and then terminates) with On-Demand Instances for the primary and core nodes, and Spot Instances for the task nodes. Here's why:

Primary and core nodes on On-Demand Instances: These nodes are critical because they manage the cluster and store data on HDFS. Running them on On-Demand Instances ensures stability and that no data is lost, as Spot Instances can be interrupted.

Task nodes on Spot Instances: Task nodes handle additional processing and can be used with Spot Instances to reduce costs. Spot Instances are much cheaper but can be interrupted, which is fine for non-critical tasks as the framework can handle retries.

A transient cluster is more cost-effective than a long-running cluster for workloads that only run for 6 hours a day. Transient clusters automatically terminate after the workload completes, saving costs by not keeping the cluster running when it's not needed.

Option A: A long-running cluster may result in unnecessary costs when the cluster isn't being used.

Option C: Running core nodes on Spot Instances risks data loss if the Spot Instances are interrupted, violating the requirement for zero data loss.

Option D: Running both core and task nodes on Spot Instances is highly risky for data-critical workloads.

AWS References:

Amazon EMR Cluster Management

Using Spot Instances in EMR

To ensure high availability and scalability, the web application should run in an Auto Scaling group across two Availability Zones behind an Application Load Balancer (ALB). The database should be migrated to Amazon RDS with Multi-AZ deployment, which ensures fault tolerance and automatic failover in case of an AZ failure. This setup minimizes administrative overhead while meeting the company's requirements for high availability and scalability.

Option A: Read replicas are typically used for scaling read operations, and Multi-AZ provides better availability for a transactional database.

Option B: Replicating across AWS Regions adds unnecessary complexity for a single web application.

Option D: EC2 instances across three Availability Zones add unnecessary complexity for this scenario.

AWS References:

Auto Scaling Groups

Amazon RDS Multi-AZ

https://computingforgeeks.com/stream-logs-in-aws-from-cloudwatch-to-elasticsearch/

This solution meets the requirements of moving the files automatically, running Lambda functions on the copied data, and sending the data files to SageMaker Pipelines with the least operational overhead. S3 replication can copy the files from the initial S3 bucket to the analysis S3 bucket as they arrive. The analysis S3 bucket can send event notifications to Amazon EventBridge (Amazon CloudWatch Events) when an object is created. EventBridge can trigger Lambda and SageMaker Pipelines as targets for the ObjectCreated rule. Lambda can run pattern-matching code on the copied data, and SageMaker Pipelines can execute a pipeline with the data files.

Option A is incorrect because creating a Lambda function to copy the files to the analysis S3 bucket is not necessary when S3 replication can do that automatically. It also adds operational overhead to manage the Lambda function. Option B is incorrect because creating a Lambda function to copy the files to the analysis S3 bucket is not necessary when S3 replication can do that automatically. It also adds operational overhead to manage the Lambda function. Option C is incorrect because using S3 event notification with multiple destinations can result in throttling or delivery failures if there are too many events.

References:

https://aws.amazon.com/blogs/machine-learning/automate-feature-engineering-pipelines-with-amazon-sagemaker/

https://docs.aws.amazon.com/sagemaker/latest/dg/automating-sagemaker-with-eventbridge.html

https://aws.amazon.com/about-aws/whats-new/2021/04/new-options-trigger-amazon-sagemaker-pipeline-executions/

For standard accelerators, Global Accelerator uses the AWS global network to route traffic to the optimal regional endpoint based on health, client location, and policies that you configure, which increases the availability of your applications. Endpoints for standard accelerators can be Network Load Balancers, Application Load Balancers, Amazon EC2 instances, or Elastic IP addresses that are located in one AWS Region or multiple Regions.

https://docs.aws.amazon.com/global-accelerator/latest/dg/what-is-global-accelerator.html

Understanding the Requirement: The company needs to copy files generated by an on-premises application to AWS without modifying the application code. The files are stored on an SMB file share and require a low-latency connection to the application servers.

Analysis of Options:

Amazon Elastic File System (EFS): EFS is designed for Linux-based workloads and does not natively support SMB file shares.

Amazon FSx for Windows File Server: FSx supports SMB file shares but would require changes to the application or additional infrastructure to connect on-premises systems.

AWS Snowball: Suitable for large data transfers but not for continuous, low-latency file copying.

AWS Storage Gateway: Provides a hybrid cloud storage solution, supporting SMB file shares and enabling seamless copying of files to AWS without requiring changes to the application.

Best Solution:

AWS Storage Gateway: This service meets the requirement for a low-latency, seamless file transfer solution from on-premises to AWS without modifying the application code.

References:

AWS Storage Gateway

Amazon FSx for Windows File Server

https://aws.amazon.com/blogs/compute/microservice-delivery-with-amazon-ecs-and-application-load-balancers/

Amazon CloudFront is a content delivery network (CDN) that speeds up the delivery of static and dynamic web content, such as HTML pages, images, and videos. By using CloudFront, the HTML pages will be served to users from the edge location that is closest to them, resulting in faster delivery and a better user experience. CloudFront can also handle the high traffic and large number of requests expected for the global event, ensuring that the HTML pages are available and accessible to users around the world.

To meet the new requirement of transferring files over a private route, the EC2 instances should be moved to private subnets, which do not have direct access to the internet. This ensures that the traffic for file transfers does not go over the internet. To enable the EC2 instances to access Amazon S3, a VPC endpoint for Amazon S3 can be created. VPC endpoints allow resources within a VPC to communicate with resources in other services without the traffic being sent over the internet. By linking the VPC endpoint to the route table for the private subnets, the EC2 instances can access Amazon S3 over a private connection within the VPC.

https://docs.aws.amazon.com/autoscaling/application/userguide/application-auto-scaling-target-tracking.html

This solution meets the requirements of a disaster recovery solution to back up the data that is generated by an analytics application, stored in JSON format, and must be accessible in milliseconds if it is needed. Amazon S3 Standard is a durable and scalable storage class for frequently accessed data. It can store any amount of data and provide high availability and performance. It can also support millisecond access time for data retrieval.

Option A is incorrect because Amazon OpenSearch Service (Amazon Elasticsearch Service) is a search and analytics service that can index and query data, but it is not a backup solution for data stored in JSON format. Option B is incorrect because Amazon S3 Glacier is a low-cost storage class for data archiving and long-term backup, but it does not support millisecond access time for data retrieval. Option D is incorrect because Amazon RDS for PostgreSQL is a relational database service that can store and query structured data, but it is not a backup solution for data stored in JSON format.

References:

https://aws.amazon.com/s3/storage-classes/

https://aws.amazon.com/s3/faqs/#Durability_and_data_protection

AWS Database Migration Service supports homogeneous migrations such as Oracle to Oracle, as well as heterogeneous migrations between different database platforms, such as Oracle or Microsoft SQL Server to Amazon Aurora. With AWS Database Migration Service, you can also continuously replicate data with low latency from any supported source to any supported target. For example, you can replicate from multiple sources to Amazon Simple Storage Service (Amazon S3) to build a highly available and scalable data lake solution. You can also consolidate databases into a petabyte-scale data warehouse by streaming data to Amazon Redshift. Learn more about the supported source and target databases. https://aws.amazon.com/dms/

In compliance mode, a protected object version can't be overwritten or deleted by any user, including the root user in your AWS account. When an object is locked in compliance mode, its retention mode can't be changed, and its retention period can't be shortened. Compliance mode helps ensure that an object version can't be overwritten or deleted for the duration of the retention period. In governance mode, users can't overwrite or delete an object version or alter its lock settings unless they have special permissions. With governance mode, you protect objects against being deleted by most users, but you can still grant some users permission to alter the retention settings or delete the object if necessary. In Governance mode, Objects can be deleted by some users with special permissions, this is against the requirement.

Compliance:

- Object versions can't be overwritten or deleted by any user, including the root user

- Objects retention modes can't be changed, and retention periods can't be shortened

Governance:

- Most users can't overwrite or delete an object version or alter its lock settings

- Some users have special permissions to change the retention or delete the object

AWS Global Accelerator and Amazon CloudFront are separate services that use the AWS global network and its edge locations around the world. CloudFront improves performance for both cacheable content (such as images and videos) and dynamic content (such as API acceleration and dynamic site delivery). Global Accelerator improves performance for a wide range of applications over TCP or UDP by proxying packets at the edge to applications running in one or more AWS Regions. Global Accelerator is a good fit for non-HTTP use cases, such as gaming (UDP), IoT (MQTT), or Voice over IP, as well as for HTTP use cases that specifically require static IP addresses or deterministic, fast regional failover. Both services integrate with AWS Shield for DDoS protection.

You can use CloudFront to deliver video on demand (VOD) or live streaming video using any HTTP origin. One way you can set up video workflows in the cloud is by using CloudFront together with AWS Media Services. https://docs.aws.am azon.com/AmazonCloudFront/latest/DeveloperGuide/on-demand-streaming-video.html

https://aws.amazon.com/about-aws/whats-new/2017/10/aws-waf-now-supports-geographic-match/

To protect data in an S3 bucket from accidental deletion, versioning should be enabled, which enables you to preserve, retrieve, and restore every version of every object in an S3 bucket. Additionally, enabling MFA (multi-factor authentication) Delete on the S3 bucket adds an extra layer of protection by requiring an authentication token in addition to the user's access keys to delete objects in the bucket.

https://docs.aws.amazon.com/IAM/latest/UserGuide/reference_aws-services-that-work-with-iam.html

SSE-S3 - is free and uses AWS owned CMKs (CMK = Customer Master Key). The encryption key is owned and managed by AWS, and is shared among many accounts. Its rotation is automatic with time that varies as shown in the table here. The time is not explicitly defined.

SSE-KMS - has two flavors:

AWS managed CMK. This is free CMK generated only for your account. You can only view it policies and audit usage, but not manage it. Rotation is automatic - once per 1095 days (3 years),

Customer managed CMK. This uses your own key that you create and can manage. Rotation is not enabled by default. But if you enable it, it will be automatically rotated every 1 year. This variant can also use an imported key material by you. If you create such key with an imported material, there is no automated rotation. Only manual rotation.

SSE-C - customer provided key. The encryption key is fully managed by you outside of AWS. AWS will not rotate it.

This solution meets the requirements of moving data to an Amazon S3 bucket, encrypting the data when it is stored in the S3 bucket, and automatically rotating the encryption key every year with the least operational overhead. AWS Key Management Service (AWS KMS) is a service that enables you to create and manage encryption keys for your data. A customer managed key is a symmetric encryption key that you create and manage in AWS KMS. You can enable automatic key rotation for a customer managed key, which means that AWS KMS generates new cryptographic material for the key every year. You can set the S3 bucket’s default encryption behavior to use the customer managed KMS key, which means that any object that is uploaded to the bucket without specifying an encryption method will be encrypted with that key.

Option A is incorrect because using server-side encryption with Amazon S3 managed encryption keys (SSE-S3) does not allow you to control or manage the encryption keys. SSE-S3 uses a unique key for each object, and encrypts that key with a master key that is regularly rotated by S3. However, you cannot enable or disable key rotation for SSE-S3 keys, or specify the rotation interval. Option C is incorrect because manually rotating the KMS key every year can increase the operational overhead and complexity, and it may not meet the requirement of rotating the key every year if you forget or delay the rotation process. Option D is incorrect because encrypting the data with customer key material before moving the data to the S3 bucket can increase the operational overhead and complexity, and it may not provide consistent encryption for all objects in the bucket. Creating a KMS key without key material and importing the customer key material into the KMS key can enable you to use your own source of random bits to generate your KMS keys, but it does not support automatic key rotation.

References:

https://docs.aws.amazon.com/kms/latest/developerguide/concepts.html

https://docs.aws.amazon.com/kms/latest/developerguide/rotate-keys.html

https://docs.aws.amazon.com/AmazonS3/latest/userguide/bucket-encryption.html

https://docs.aws.amazon.com/IAM/latest/UserGuide/id_roles.html https://docs.aws.amazon.com/IA M/latest/UserGuide/id_users.html

Understanding the Requirement: The application running on EC2 instances in private subnets needs to securely connect to an Amazon SQS queue without exposing traffic to the public internet.

Analysis of Options:

Interface VPC Endpoint in Private Subnets: Allows private, secure connectivity to SQS without using the public internet. Configuring security groups ensures controlled access from EC2 instances.

Interface VPC Endpoint in Public Subnets: Not necessary for private EC2 instances and exposes additional security risks.

Gateway Endpoint: Gateway endpoints are not supported for SQS; they are used for services like S3 and DynamoDB.

NAT Gateway with IAM Role: Increases costs and complexity compared to using an interface VPC endpoint directly.

Best Solution:

Interface VPC Endpoint in Private Subnets: This option ensures secure, private connectivity to SQS, meeting the requirement with minimal complexity and optimal security.

References:

VPC Endpoints

Amazon SQS and VPC Endpoints

Amazon S3 is cheapest and can be accessed from anywhere.

This solution meets the requirements of a two-tier application that has a variable demand based on the time of day and must be available at all times, while minimizing the overall cost. EC2 Reserved Instances can provide significant savings compared to On-Demand Instances for the baseline level of usage, and they can guarantee capacity reservation when needed. EC2 Spot Instances can provide up to 90% savings compared to On-Demand Instances for any additional capacity that the application needs during peak hours. Spot Instances are suitable for stateless applications that can tolerate interruptions and can be replaced by other instances. Stopping the RDS database when it is not in use can reduce the cost of running the database tier.

Option A is incorrect because using all EC2 Spot Instances can affect the availability of the application if there are not enough spare capacity or if the Spot price exceeds the maximum price. Stopping the RDS database when it is not in use can reduce the cost of running the database tier, but it can also affect the availability of the application. Option B is incorrect because purchasing EC2 Instance Savings Plans to cover five EC2 instances can lock in a fixed amount of compute usage per hour, which may not match the actual usage pattern of the application. Purchasing an RDS Reserved DB Instance can provide savings for the database tier, but it does not allow stopping the database when it is not in use. Option D is incorrect because purchasing EC2 Instance Savings Plans to cover two EC2 instances can lock in a fixed amount of compute usage per hour, which may not match the actual usage pattern of the application. Using up to three additional EC2 On-Demand Instances as needed can incur higher costs than using Spot Instances.

References:

https://aws.amazon.com/ec2/pricing/reserved-instances/

https://aws.amazon.com/ec2/spot/

https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/USER_StopInstance.html

https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/rds-custom.html and https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/working-with-custom-oracle.html

https://aws.amazon.com/blogs/networking-and-content-delivery/deliver-your-apps-dynamic-content-using-amazon-cloudfront-getting-started-template/

Using AWS WAF has several benefits. Additional protection against web attacks using criteria that you specify. You can define criteria using characteristics of web requests such as the following: Presence of SQL code that is likely to be malicious (known as SQL injection). Presence of a script that is likely to be malicious (known as cross-site scripting). AWS Firewall Manager simplifies your administration and maintenance tasks across multiple accounts and resources for a variety of protections. https://docs.aws.amazon.com/waf/latest/developerguide/what-is-aws-waf.html

EC2 Spot Instances allow users to bid on spare Amazon EC2 computing capacity and can be a cost-effective solution for stateless, interruptible workloads that can be started and stopped at any time. Since the batch processing job is stateless, can be started and stopped at any time, and typically takes upwards of 60 minutes to complete, EC2 Spot Instances would be a good fit for this workload.

https://docs.aws.amazon.com/lambda/latest/dg/configuration-vpc.html#vpc-managing-eni

In a typical development environment, dev and test databases are mostly utilized for 8 hours a day and sit idle when not in use. However, the databases are billed for the compute and storage costs during this idle time. To reduce the overall cost, Amazon RDS allows instances to be stopped temporarily. While the instance is stopped, you’re charged for storage and backups, but not for the DB instance hours. Please note that a stopped instance will automatically be started after 7 days. This post presents a solution using AWS Lambda and Amazon EventBridge that allows you to schedule a Lambda function to stop and start the idle databases with specific tags to save on compute costs. The second post presents a solution that accomplishes stop and start of the idle Amazon RDS databases using AWS Systems Manager.

https://docs.aws.amazon.com/AmazonRDS/latest/UserGu ide/USER_RestoreFromSnapshot.html#USER_RestoreFromSnapshot.CON

Under "Encrypt unencrypted resources" - https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/EBSEncryptio n.html

https://aws.amazon.com/about-aws/whats-new/2018/04/announcing-s3-one-zone-infrequent-access-a-new-amazon-s3-storage-class/?nc1=h_ls

1. lowest possible latency + node to node ==> cluster placement(must be within one AZ), so C, D out

2. For EBS Multi-Attach, up to 16 instances can be attached to a single volume==>we have 16 linux instance==>more close to A

3. "need a shared block device volume"==>EBS Multi-attach is Block Storage whereas EFS is File Storage==> B out

4. EFS automatically replicates data within and across 3 AZ==>we use cluster placement so all EC2 are within one AZ.

5. EBS Multi-attach volumes can be used for clients within a single AZ.

https://repost.aws/questions/QUK2RANw1QTKCwpDUwCCI72A/efs-vs-ebs-mult-attach

https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/InstanceStorage.html

https://docs.aws.amazon.com/AmazonRDS/late st/AuroraUserGuide/Aurora.Replication.html

#Aurora.Replication.Replicas Aurora Replicas have two main purposes. You can issue queries to them to scale the read operations for your application. You typically do so by connecting to the reader endpoint of the cluster. That way, Aurora can spread the load for read-only connections across as many Aurora Replicas as you have in the cluster. Aurora Replicas also help to increase availability. If the writer instance in a cluster becomes unavailable, Aurora automatically promotes one of the reader instances to take its place as the new writer.

https://docs.aws.amazon.com/Amazon RDS/latest/AuroraUserGuide/Aurora.Overview.html

This solution meets the requirements of a customer-facing application that has a clearly defined access pattern throughout the year and a variable number of reads and writes that depend on the time of year. Amazon DynamoDB is a fully managed NoSQL database service that can handle any level of request traffic and data size. DynamoDB auto scaling can automatically adjust the provisioned read and write capacity based on the actual workload. DynamoDB on-demand backups can create full backups of the tables for data protection and archival purposes. DynamoDB Streams can capture a time-ordered sequence of item-level modifications in the tables for audit purposes.

Option B is incorrect because Amazon Redshift is a data warehouse service that is designed for analytical workloads, not for customer-facing applications. Option C is incorrect because Amazon RDS with Provisioned IOPS can provide consistent performance for relational databases, but it may not be able to handle unpredictable spikes in traffic and data size. Option D is incorrect because Amazon Aurora MySQL with auto scaling can provide high performance and availability for relational databases, but it does not support audit logging as a parameter.

References:

https://aws.amazon.com/dynamodb/

https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/AutoScaling.html

https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/BackupRestore.html

https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/Streams.html

https://docs.aws.amazon.com/Route53/latest/DeveloperGuide/dns-failover-types.html

https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-ec2-placementgroup.html

"A cluster placement group is a logical grouping of instances within a single Availability Zone that benefit from low network latency, high network throughput"

Q: What does Amazon RDS manage on my behalf?

Amazon RDS manages the work involved in setting up a relational database: from provisioning the infrastructure capacity you request to installing the database software. Once your database is up and running, Amazon RDS automates common administrative tasks such as performing backups and patching the software that powers your database. With optional Multi-AZ deployments, Amazon RDS also manages synchronous data replication across Availability Zones with automatic failover.

https://aws.amazon.com/rds/faqs/

AWS Shield Advanced provides expanded DDoS attack protection for your Amazon EC2 instances, Elastic Load Balancing load balancers, CloudFront distributions, Route 53 hosted zones, and AWS Global Accelerator standard accelerators. https://docs.aws.amazon.com/waf/latest/developerguide/what-is-aws-waf.html

Queue has Limited throughput (300 msg/s without batching, 3000 msg/s with batching whereby up-to 10 msg per batch operation; Msg duplicates not allowed in the queue (exactly-once delivery); Msg order is preserved (FIFO); Queue name must end with .fifo

Composite alarms determine their states by monitoring the states of other alarms. You can **use composite alarms to reduce alarm noise**. For example, you can create a composite alarm where the underlying metric alarms go into ALARM when they meet specific conditions. You then can set up your composite alarm to go into ALARM and send you notifications when the underlying metric alarms go into ALARM by configuring the underlying metric alarms never to take actions. Currently, composite alarms can take the following actions: https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/Create_Composite_Alarm.html

Reserved is cheaper than on demand the company has. And it's meet the availabilty (HA) requirement as to spot instance that can be disrupted at any time. PRICING BELOW. On-Demand: 0% There’s no commitment from you. You pay the most with this option. Reserved : 40%-60%1-year or 3-year commitment from you. You save money from that commitment. Spot 50%-90% Ridiculously inexpensive because there’s no commitment from the AWS side.

https://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/private-content-restricting-access-to-s3.html

https://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/distribution-web- awswaf.html

https://aws.amazon.com/dynamodb/dax/

Amazon Athena is the best choice for running one-time queries on streaming data. Although Amazon Kinesis Data Analytics provides an easy and familiar standard SQL language to analyze streaming data in real-time, it is designed for continuous queries rather than one-time queries[1]. On the other hand, Amazon Athena is a serverless interactive query service that allows querying data in Amazon S3 using SQL. It is optimized for ad-hoc querying and is ideal for running one-time queries on streaming data[2].AWS Lake Formation uses as a central place to have all your data for analytics purposes (E). Athena integrate perfect with S3 and can makes queries (A).

To store and access files that are 25 GB or larger across many EC2 instances and across multiple Availability Zones, Amazon Elastic File System (Amazon EFS) is a suitable solution. Amazon EFS provides a simple, scalable, elastic file system that can be mounted on multiple EC2 instances concurrently. Amazon EFS supports high availability and durability by storing data across multiple Availability Zones within a Region.

References:

What Is Amazon Elastic File System?

Using EFS with EC2

This solution meets the requirements of a serverless solution, encryption, replication, and SQL analysis with the least operational overhead. Amazon Athena is a serverless interactive query service that can analyze data in S3 using standard SQL. S3 Cross-Region Replication (CRR) can replicate encrypted objects to an S3 bucket in another Region automatically. Server-side encryption with AWS KMS multi-Region keys (SSE-KMS) can encrypt the data at rest using keys that are replicated across multiple Regions. Creating a new S3 bucket can avoid potential conflicts with existing data or configurations.

Option B is incorrect because Amazon RDS is not a serverless solution and it cannot query data in S3 directly. Option C is incorrect because server-side encryption with Amazon S3 managed encryption keys (SSE-S3) does not use KMS keys and it does not support multi-Region replication. Option D is incorrect because Amazon RDS is not a serverless solution and it cannot query data in S3 directly. It is also incorrect for the same reason as option C.

References:

https://docs.aws.amazon.com/AmazonS3/latest/userguide/replication-walkthrough-4.html

https://aws.amazon.com/blogs/storage/considering-four-different-replication-options-for-data-in-amazon-s3/

https://docs.aws.amazon.com/AmazonS3/latest/userguide/UsingEncryption.html

https://aws.amazon.com/athena/

To migrate a MySQL database to AWS with compatibility and scalability, Amazon Aurora is a suitable option. Aurora is compatible with MySQL and can scale automatically with Aurora Auto Scaling. AWS Database Migration Service (AWS DMS) can be used to migrate the database from on-premises to Aurora with minimal downtime.

References:

What Is Amazon Aurora?

Using Amazon Aurora Auto Scaling with Aurora Replicas

What Is AWS Database Migration Service?

AWS Fargate is a serverless experience for user applications, allowing the user to concentrate on building applications instead of configuring and managing servers. Fargate also automates resource management, allowing users to easily scale their applications in response to demand.

Amazon Aurora is a fully managed relational database that is compatible with MySQL and PostgreSQL. It provides up to five times better performance than MySQL and up to three times better performance than PostgreSQL. It also provides high availability and durability by replicating data across multiple Availability Zones and continuously backing up data to Amazon S31. By using Amazon RDS deployed in a Multi-AZ instance deployment to create an Amazon Aurora database, the solution can achieve higher availability and improve application performance.

Amazon Aurora supports read replicas, which are separate instances that share the same underlying storage as the primary instance. Read replicas can be used to offload read-only queries from the primary instance and improve performance. Read replicas can also be used for reporting functions2. By directing the reporting functions to the reader instances, the solution can offload reporting from its primary database system.

A. Use AWS Database Migration Service (AWS DMS) to create an Amazon RDS DB instance in multiple AWS Regions Point the reporting functions toward a separate DB instance from the pri-mary DB instance. This solution will not meet the requirement of using an AWS database service, as AWS DMS is a service that helps users migrate databases to AWS, not a database service itself. It also involves creating multiple DB instances in different Regions, which may increase complexity and cost.

B. Use Amazon RDS in a Single-AZ deployment to create an Oracle database Create a read replica in the same zone as the primary DB instance. Direct the reporting functions to the read replica. This solution will not meet the requirement of achieving higher availability, as a Single-AZ deployment does not provide failover protection in case of an Availability Zone outage. It also involves using Oracle as the database engine, which may not provide better performance than Aurora.

C. Use Amazon RDS deployed in a Multi-AZ cluster deployment to create an Oracle database Di-rect the reporting functions to use the reader instance in the cluster deployment. This solution will not meet the requirement of improving application performance, as Oracle may not provide better performance than Aurora. It also involves using a cluster deployment, which is only supported for Aurora, not for Oracle.

Reference URL: https://aws.amazon.com/rds/aurora/

These answers are correct because they reflect the customer’s responsibilities for using AWS Outposts as part of the solution. According to the AWS shared responsibility model, the customer is responsible for providing resilient power and network connectivity to the Outposts racks, ensuring physical security and access controls of the data center environment, and providing extra capacity for Amazon ECS clusters to mitigate server failures and maintenance events. AWS is responsible for managing the virtualization hypervisor, storage systems, and the AWS services that run on Outposts, as well as the availability of the Outposts infrastructure including the power supplies, servers, and networking equipment within the Outposts racks, and the physical maintenance of Outposts components.

References:

https://docs.aws.amazon.com/outposts/latest/userguide/what-is-outposts.html

https://www.contino.io/insights/the-sandwich-responsibility-model-aws-outposts/

The solution that meets the requirements of high availability, performance, security, and static IP addresses is to use Amazon CloudFront, Application Load Balancers (ALBs), Amazon Route 53, and AWS WAF. This solution allows the company to distribute its HTTP-based application globally using CloudFront, which is a content delivery network (CDN) service that caches content at edge locations and provides static IP addresses for each edge location. The company can also use Route 53 latency-based routing to route requests to the closest ALB in each Region, which balances the load across the EC2 instances. The company can also deploy AWS WAF on the CloudFront distribution to protect the application against common web exploits by creating rules that allow, block, or count web requests based on conditions that are defined. The other solutions do not meet all the requirements because they either use Network Load Balancers (NLBs), which do not support HTTP-based applications, or they do not use CloudFront, which provides better performance and security than AWS Global Accelerator. References :=

Amazon CloudFront

Application Load Balancer

Amazon Route 53

AWS WAF

https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/security-group-rules.html

A function URL is a unique identifier for a Lambda function that can be used to invoke the function over HTTPS. It is composed of the API endpoint of the AWS Region where the function is deployed, and the name or ARN of the function1. By creating a function URL for the Lambda function, the solution can make the Lambda function available for the third party to call with the most operational efficiency.

B. Deploy an Application Load Balancer (ALB) in front of the Lambda function. Provide the ALB URL to the third party for the webhook. This solution will not meet the requirement of the most operational efficiency, as it involves creating and managing an additional resource (ALB) that is not necessary for invoking a Lambda function over HTTPS2.

C. Create an Amazon Simple Notification Service (Amazon SNS) topic. Attach the topic to the Lambda function. Provide the public hostname of the SNS topic to the third party for the webhook. This solution will not work, as Amazon SNS topics do not have public hostnames that can be used as webhooks. SNS topics are used to publish messages to subscribers, not to receive messages from external sources3.

D. Create an Amazon Simple Queue Service (Amazon SQS) queue. Attach the queue to the Lamb-da function. Provide the public hostname of the SQS queue to the third party for the webhook. This solution will not work, as Amazon SQS queues do not have public hostnames that can be used as webhooks. SQS queues are used to send, store, and receive messages between AWS services, not to receive messages from external sources.

Reference URL: https://docs.aws.amazon.com/lambda/latest/dg/lambda-api-permissions-ref.html

This answer is correct because it meets the requirements of securely migrating the existing data to AWS and satisfying the new regulation. AWS DataSync is a service that makes it easy to move large amounts of data online between on-premises storage and Amazon S3. DataSync automatically encrypts data in transit and verifies data integrity during transfer. AWS CloudTrail is a service that records AWS API calls for your account and delivers log files to Amazon S3. CloudTrail can log data events, which show the resource operations performed on or within a resource in your AWS account, such as S3 object-level API activity. By using CloudTrail to log data events, you can audit access at all levels of the stored data.

References:

https://docs.aws.amazon.com/datasync/latest/userguide/what-is-datasync.html

https://docs.aws.amazon.com/awscloudtrail/latest/userguide/logging-data-events-with-cloudtrail.html

it allows the company to migrate its data from the on-premises location to an Amazon S3 bucket and automatically validate the integrity of the data after the transfer. By deploying an AWS DataSync agent on premises, the company can use a fully managed data transfer service that makes it easy to move large amounts of data to and from AWS. By configuring the DataSync agent to perform the online data transfer to an S3 bucket, the company can take advantage of DataSync’s features, such as encryption, compression, bandwidth throttling, and data validation. DataSync automatically verifies data integrity at both source and destination after each transfer task. References:

AWS DataSync

Deploying an Agent for AWS DataSync

How AWS DataSync Works

it allows the solutions architect to review the S3 buckets to discover personally identifiable information (Pll) with the least operational overhead. Amazon Macie is a fully managed data security and data privacy service that uses machine learning and pattern matching to discover and protect sensitive data in AWS. Amazon Macie can analyze data in S3 buckets across multiple regions and provide insights into the type, location, and level of sensitivity of the data. References:

Amazon Macie

Analyzing data with Amazon Macie

Understanding the Requirement: The application needs to write to multiple block storage volumes in multiple EC2 Nitro-based instances simultaneously to achieve higher availability.

Analysis of Options:

General Purpose SSD (gp3) with Multi-Attach: Supports Multi-Attach but does not provide the highest performance required for critical applications.

Throughput Optimized HDD (st1) with Multi-Attach: Not suitable for applications requiring high performance and low latency.

Provisioned IOPS SSD (io2) with Multi-Attach: Provides high performance and durability, suitable for applications requiring simultaneous writes and high availability.

General Purpose SSD (gp2) with Multi-Attach: Similar to gp3 but with less flexibility and performance.

Best Solution:

Provisioned IOPS SSD (io2) with Multi-Attach: This solution ensures the highest performance and availability for the application by allowing multiple EC2 instances to attach to and write to the same EBS volume simultaneously.

References:

Amazon EBS Multi-Attach

Provisioned IOPS SSD (io2)

https://docs.a ws.amazon.com/prescriptive-guidance/latest/patterns/three-aws-glue-etl-job-types-for-converting-data-to-apache-parquet.html

S3 Storage Lens is a cloud storage analytics feature that provides organization-wide visibility into object storage usage and activity across multiple AWS accounts in AWS Organizations. S3 Storage Lens can report the incomplete multipart upload object count as one of the metrics that it collects and displays on an interactive dashboard in the S3 console. S3 Storage Lens can also export metrics in CSV or Parquet format to an S3 bucket for further analysis. This solution will meet the requirements with the least operational overhead, as it does not require any code development or policy changes.

References:

1 explains how to use S3 Storage Lens to gain insights into S3 storage usage and activity.

2 describes the concept and benefits of multipart uploads.

Consider using the default aws/s3 KMS key if: You're uploading or accessing S3 objects using AWS Identity and Access Management (IAM) principals that are in the same AWS account as the AWS KMS key. You don't want to manage policies for the KMS key. Consider using a customer managed key if: You want to create, rotate, disable, or define access controls for the key. You want to grant cross-account access to your S3 objects. You can configure the policy of a customer managed key to allow access from another account. https://repost.aws/knowledge-center/s3-object-encryption-keys

To achieve high availability for an application that runs on EC2 instances, the application should be deployed across multiple Availability Zones and use a load balancer to distribute traffic. An Auto Scaling group can be used to launch and manage EC2 instances in multiple Availability Zones and perform health checks on them. A Network Load Balancer can be used to handle transport layer traffic to the EC2 instances.

References:

Auto Scaling Groups

What Is a Network Load Balancer?

User-defined tags are key-value pairs that can be applied to AWS resources to categorize and track them. User-defined tags can also be used to allocate costs and create detailed billing reports in the AWS Billing console. To use user-defined tags for cost allocation, the tags must be activated from the Organizations management account, which is the root account that has full control over all the member accounts in the organization. Once activated, the user-defined tags will appear as columns in the cost allocation report, and can be used to filter and group costs by product line. This solution will meet the requirements with the least operational overhead, as it leverages the existing tagging strategy and does not require any code development or manual intervention.

References:

1 explains how to use user-defined tags for cost allocation.

2 describes how to access and manage member accounts from the Organizations management account.

3 discusses how to create and view cost allocation reports in the AWS Billing console.

Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. You can start with just a few hundred gigabytes of data and scale to a petabyte or more. This allows you to use your data to gain new insights for your business and customers. The first step to create a data warehouse is to launch a set of nodes, called an Amazon Redshift cluster. After you provision your cluster, you can upload your data set and then perform data analysis queries. Regardless of the size of the data set, Amazon Redshift offers fast query performance using the same SQL-based tools and business intelligence applications that you use today.

This answer is correct because it improves the application performance and decreases latency for the online game by using AWS Global Accelerator. AWS Global Accelerator is a networking service that helps you improve the availability, performance, and security of your public applications. Global Accelerator provides two global static public IPs that act as a fixed entry point to your application endpoints, such as NLBs, in different AWS Regions. Global Accelerator uses the AWS global network to route traffic to the optimal regional endpoint based on health, client location, and policies that you configure. Global Accelerator also terminates TCP and UDP traffic at the edge locations, which reduces the number of hops and improves the network performance. By adding AWS Global Accelerator in front of the NLBs, you can achieve up to 60% improvement in latency for your online game.

References:

https://docs.aws.amazon.com/global-accelerator/latest/dg/what-is-global-accelerator.html

https://aws.amazon.com/global-accelerator/

The Kubernetes Cluster Autoscaler is a component that automatically adjusts the number of nodes in your cluster when pods fail or are rescheduled onto other nodes. It uses Auto Scaling groups to scale up or down the nodes according to the demand and capacity of your cluster1.

By using the Kubernetes Cluster Autoscaler in your Amazon EKS cluster, you can achieve the following benefits:

You can improve the performance and availability of your container applications by ensuring that there are enough nodes to run your pods and that there are no idle nodes wasting resources.

You can reduce the administrative overhead of managing your cluster size manually or using custom scripts. The Cluster Autoscaler handles the scaling decisions and actions for you based on the metrics and events from your cluster.

You can leverage the integration of Amazon EKS and AWS Auto Scaling to optimize the cost and efficiency of your cluster. You can use features such as launch templates, mixed instances policies, and spot instances to customize your node configuration and save up to 90% on compute costs2

AWS Cost Anomaly Detection allows you to create monitors that track the cost and usage of your AWS resources and alert you when there is an unusual spending pattern. You can create monitors based on different dimensions, such as AWS services, accounts, tags, or cost categories. You can also create alert subscriptions that notify you by email or Amazon SNS when an anomaly is detected. You can specify the threshold and frequency of the alerts, and choose to receive weekly summaries of your anomalies.

Reference URLs:

1 https://aws.amazon.com/aws-cost-management/aws-cost-anomaly-detection/

2 https://docs.aws.amazon.com/cost-management/latest/userguide/getting-started-ad.html

3 https://docs.aws.amazon.com/cost-management/latest/userguide/manage-ad.html

This answer is correct because it meets the requirements of releasing the new version of APIs with minimal effects on customers and minimal data loss. A canary release deployment is a software development strategy in which a new version of an API is deployed for testing purposes, and the base version remains deployed as a production release for normal operations on the same stage. In a canary release deployment, total API traffic is separated at random into a production release and a canary release with a pre-configured ratio. Typically, the canary release receives a small percentage of API traffic and the production release takes up the rest. The updated API features are only visible to API traffic through the canary. You can adjust the canary traffic percentage to optimize test coverage or performance. By keeping canary traffic small and the selection random, most users are not adversely affected at any time by potential bugs in the new version, and no single user is adversely affected all the time. After the test metrics pass your requirements, you can promote the canary release to the production release and disable the canary from the deployment. This makes the new features available in the production stage.

References:

https://docs.aws.amazon.com/apigateway/latest/developerguide/canary-release.html

https://doc s.aws.amazon.com/eks/latest/userguide/horizontal-pod-autoscaler.html

https://docs.aws.amazon.com/eks/latest/userguide/autoscaling.html

Horizontal pod autoscaling is a feature of Kubernetes that automatically scales the number of pods in a deployment, replication controller, or replica set based on that resource’s CPU utilization. It requires a metrics source such as the Kubernetes Metrics Server to provide CPU usage data1. Cluster autoscaling is a feature of Kubernetes that automatically adjusts the number of nodes in a cluster when pods fail or are rescheduled onto other nodes. It requires an integration with AWS Auto Scaling groups to manage the EC2 instances that join the cluster2. By using both horizontal pod autoscaling and cluster autoscaling, the solution can ensure that Amazon EKS scales in and out according to the workload.

https://aws.amazon.com/blogs/aws/introducing-amazon-s3-object-lambda-use-your-cod e-to-process-data-as-it-is-being-retrieved-from-s3/

S3 Object Lambda is a new feature of Amazon S3 that enables customers to add their own code to process data retrieved from S3 before returning it to the application. By using S3 Object Lambda, the data can be processed and transformed in real-time, without the need to store multiple copies of the data in separate S3 buckets or DynamoDB tables.

In this case, the Pll can be removed from the data by the code added to S3 Object Lambda before returning the data to the two applications that do not need to process Pll. The one application that requires Pll can be pointed to the original S3 bucket where the Pll is still stored.

Using S3 Object Lambda is the simplest and most cost-effective solution, as it eliminates the need to maintain multiple copies of the same data in different buckets or tables, which can result in additional storage costs and operational overhead.

Joining the FSx for Windows File Server file system to the on-premises Active Directory will allow the company to use the existing Active Directory groups to restrict access to the file shares, folders, and files after the move to AWS. This option allows the company to continue using their existing access controls and management structure, making the transition to AWS more seamless.

A cross-account IAM role is a way to grant users from one AWS account access to resources in another AWS account. The cross-account IAM role can have a read-only access policy attached to it, which allows the users to download objects from the S3 buckets without modifying or deleting them. The cross-account IAM role also reduces the operational overhead of managing multiple IAM users and policies in each account. The cross-account IAM role meets all the requirements of the question, while the other options do not. References:

https://docs.aws.amazon.com/AmazonS3/latest/userguide/example-walkthroughs-managing-access-example2.html

https://aws.amazon.com/blogs/storage/setting-up-cross-account-amazon-s3-access-with-s3-access-points/

https://docs.aws.amazon.com/IAM/latest/UserGuide/id_roles_create_for-user_externalid.html

An egress-only internet gateway is a VPC component that allows outbound communication over IPv6 from instances in your VPC to the internet, and prevents the internet from initiating an IPv6 connection with your instances. This meets the company’s security policy and requirements. To use an egress-only internet gateway, you need to add a route in the subnet’s route table that routes IPv6 internet traffic (::/0) to the egress-only internet gateway.

Reference URLs:

1 https://docs.aws.amazon.com/vpc/latest/userguide/egress-only-internet-gateway.html

2 https://dev.to/aws-builders/what-is-an-egress-only-internet-gateways-in-aws-7gp

3 https://docs.aws.amazon.com/vpc/latest/userguide/route-table-options.html

it allows the company to reduce the data output costs for accessing Amazon S3 from Amazon EC2 instances in a VPC. By provisioning a VPC gateway endpoint, the company can enable private connectivity between the VPC and S3. By configuring the route table for the private subnet to use the gateway endpoint as the route for all S3 traffic, the company can avoid using a NAT gateway, which charges for data processing and data transfer. References:

VPC Endpoints for Amazon S3

VPC Endpoints Pricing

For C: IMPROVED USER EXPERIENCE Lambda@Edge can help improve your users' experience with your websites and web applications across the world, by letting you personalize content for them without sacrificing performance. Real-time Image Transformation You can customize your users' experience by transforming images on the fly based on the user characteristics. For example, you can resize images based on the viewer's device type—mobile, desktop, or tablet. You can also cache the transformed images at CloudFront Edge locations to further improve performance when delivering images. https://aws.amazon.com/lambda/edge/

it allows the application EC2 instances to access the DynamoDB tables without exposing API credentials in the template. By creating an IAM role that has the required permissions to read and write from the DynamoDB tables and adding it to the EC2 instance profile, the application instances can use temporary security credentials that are automatically rotated by AWS. This is a secure and best practice way to grant access to AWS resources from EC2 instances. References:

IAM Roles for Amazon EC2

Using Instance Profiles

To ingest and process high-volume streaming data with the least operational overhead, Amazon Kinesis Data Firehose is a suitable solution. Amazon Kinesis Data Firehose can capture, transform, and deliver streaming data to Amazon S3 or other destinations. Amazon Kinesis Data Firehose can scale automatically to match the throughput of the data and handle any amount of data. Amazon Kinesis Data Firehose is also a fully managed service that does not require any servers to provision or manage.

References:

What Is Amazon Kinesis Data Firehose?

Amazon Kinesis Data Firehose Pricing

Amazon RDS Proxy is a fully managed, highly available database proxy for Amazon Relational Database Service (RDS) that makes applications more scalable, more resilient to database failures, and more secure1. RDS Proxy allows applications to pool and share connections established with the database, improving database efficiency and application scalability2. RDS Proxy also reduces failover times for Aurora and RDS databases by up to 66% and enables IAM authentication and Secrets Manager integration for database access1. RDS Proxy can be enabled for most applications with no code changes2.

Amazon CloudFront is a content delivery network (CDN) that can improve the performance and reduce the cost of serving static content from a website. CloudFront can cache static files at edge locations closer to the users, reducing the latency and data transfer costs. CloudFront can also integrate with Amazon S3 as the origin for the static content, eliminating the need for EC2 instances to host the website. CloudFront meets all the requirements of the question, while the other options do not. References:

https://aws.amazon.com/blogs/architecture/architecting-a-low-cost-web-content-publishing-system/

https://nodeployfriday.com/posts/static-website-hosting/

https://aws.amazon.com/cloudfront/

A service control policy (SCP) is a type of policy that you can use to manage permissions in your organization. SCPs offer central control over the maximum available permissions for all accounts in your organization, allowing you to ensure your accounts stay within your organization’s access control guidelines. You can use an SCP to deny the ec2:CreateVolume action when the ec2:Encrypted condition equals false, which means that any user or role in the accounts under the root OU will not be able to create unencrypted EBS volumes. This solution will have minimal effect on employees who create EBS volumes, as they can still create encrypted volumes as needed. References: https://doc s.aws.amazon.com/organizations/latest/userguide/orgs_manage_policies_scps.html

An IAM policy is a document that defines the permissions for an IAM identity (such as a user, group, or role). You can use IAM policies to grant permissions to existing users and groups based on department. You can create an IAM policy that grants least privilege permission, which means that you only grant the minimum permissions required for the users to perform their tasks. You can then attach the policy to the IAM groups, which will apply the policy to all the users in those groups. This solution will reduce operational costs and simplify configuration and management of permissions. References: https://docs.aws.amazon.com/IAM/latest/UserGuide/access_policies.html

You can use metric streams to continually stream CloudWatch metrics to a destination of your choice, with near-real-time delivery and low latency. One of the use cases is Data Lake: create a metric stream and direct it to an Amazon Kinesis Data Firehose delivery stream that delivers your CloudWatch metrics to a data lake such as Amazon S3. https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/CloudWatch-Metr ic-Streams.html

A VPC peering connection is a networking connection between two VPCs that enables users to route traffic between them using private IP addresses. Instances in either VPC can communicate with each other as if they are within the same network. A VPC peering connection can be created between VPCs in the same or different AWS accounts and Regions1. By configuring a VPC peering connection between VPC A and VPC B, the solution can provide the required access most securely.

A. Create a DB instance security group that allows all traffic from the public IP address of the application server in VPC A. This solution will not provide the required access most securely, as it involves exposing the DB instance to the public internet and relying on a single IP address for access control2.

C. Make the DB instance publicly accessible. Assign a public IP address to the DB instance. This solution will not provide the required access most securely, as it involves exposing the DB instance to the public internet and allowing any source to connect to it2.

D. Launch an EC2 instance with an Elastic IP address into VPC B. Proxy all requests through the new EC2 instance. This solution will not provide the required access most securely, as it involves creating an additional resource and configuring a proxy server that may introduce latency and complexity3.

Reference URL: https://docs.aws.amazon.com/vpc/latest/peering/what-is-vpc-peering.html

https://docs.aws.amazon.com/AmazonS3/latest/userguide/object-lock-overview.html

To back up EC2 instances and RDS DB instances in a separate Region with the least operational overhead, AWS Backup is a simple and cost-effective solution. AWS Backup can copy EC2 backups and RDS backups to another Region automatically and securely. AWS Backup also supports backup policies, retention rules, and monitoring features.

References:

What Is AWS Backup?

Cross-Region Backup

https://docs.aws.amazon.com/vpc/latest/privatelink/vpc-endpoints.html

 These answers are correct because they meet the requirements of converting the .csv files into images, making them available as soon as possible, and minimizing the storage costs. AWS Lambda is a service that lets you run code without provisioning or managing servers. You can use AWS Lambda to design a function that converts the .csv files into images and stores the images in the S3 bucket. You can invoke the Lambda function when a .csv file is uploaded to the S3 bucket by using an S3 event notification. This way, you can ensure that the images are generated and made available as soon as possible for the graphical reports. S3 Lifecycle is a feature that enables you to manage your objects so that they are stored cost effectively throughout their lifecycle. You can create S3 Lifecycle rules for .csv files and image files in the S3 bucket to transition them to different storage classes or expire them based on your business needs. You can transition the .csv files from S3 Standard to S3 Glacier 1 day after they are uploaded, since they are only needed twice a year for ML trainings and audits that are planned weeks in advance. S3 Glacier is a storage class for data archiving that offers secure, durable, and extremely low-cost storage with retrieval times ranging from minutes to hours. You can expire the image files after 30 days, since they become irrelevant after 1 month.

References:

https://docs.aws.amazon.com/lambda/latest/dg/welcome.html

https://docs.aws.amazon.com/AmazonS3/latest/userguide/NotificationHowTo.html

https://docs.aws.amazon.com/AmazonS3/latest/userguide/object-lifecycle-mgmt.html

https://docs.aws.amazon.com/AmazonS3/latest/userguide/storage-class-intro.html#sc-glacier

AWS Global Accelerator is a networking service that improves the performance and availability of applications for global users. It uses the AWS global network to route user traffic to the optimal endpoint based on performance and health. It also provides static IP addresses that act as a fixed entry point to the applications and support both TCP and UDP protocols1. By using AWS Global Accelerator, the solution can ensure the lowest possible latency for all users.

A. Use AWS Global Accelerator to create an accelerator. Create an Application Load Balancer (ALB) behind an accelerator endpoint that uses Global Accelerator integration and listening on the TCP and UDP ports. Update the Auto Scaling group to register instances on the ALB. This solution will not work, as ALB does not support UDP protocol2.

C. Create an Amazon CloudFront content delivery network (CDN) endpoint. Create a Network Load Balancer (NLB) behind the endpoint and listening on the TCP and UDP ports. Update the Auto Scaling group to register instances on the NLB. Update CloudFront to use the NLB as the origin. This solution will not work, as CloudFront does not support UDP protocol3.

D. Create an Amazon Cloudfront content delivery network (CDN) endpoint. Create an Application Load Balancer (ALB) behind the endpoint and listening on the TCP and UDP ports. Update the Auto Scaling group to register instances on the ALB. Update CloudFront to use the ALB as the origin. This solution will not work, as CloudFront and ALB do not support UDP protocol23.

Reference URL: https://aws.amazon.com/global-accelerator/

https://digitalcloud.training/ssh-into-ec2-in-private-subnet/

Amazon EFS is a fully managed file system that can be mounted by multiple Linux instances in AWS and on premises through native protocols such as NFS and SMB. Amazon EFS has no minimum size requirements and can scale up and down automatically as files are added and removed. Amazon EFS also supports high availability and durability by allowing multiple mount targets in different Availability Zones within a region. Amazon EFS meets all the requirements of the question, while the other options do not. References:

https://aws.amazon.com/efs/

https://docs.aws.amazon.com/wellarchitected/latest/performance-efficiency-pillar/storage-architecture-selection.html

https://aws.amazon.com/blogs/storage/from-on-premises-to-aws-hybrid-cloud-architecture-for-network-file-shares/

Option C provides moving the website images onto an Amazon EFS file system that is mounted on every EC2 instance. Amazon EFS provides a scalable and fully managed file storage solution that can be accessed concurrently from multiple EC2 instances. This ensures that the website images can be accessed efficiently and consistently by all instances, improving performance In Option E The Auto Scaling group maintains a minimum of two instances, ensuring resilience by automatically replacing any unhealthy instances. Additionally, configuring an Amazon CloudFront distribution for the website further improves performance by caching content at edge locations closer to the end-users, reducing latency and improving content delivery. Hence combining these actions, the website's performance is improved through efficient image storage and content delivery

https://aws.amazon.com/shield/faqs/

(https://aws.amazon.com/cloudfront

AWS Network Firewall supports both inspection and filtering as required

https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ec2-capacity-reservations.html

Reserve instances: You will have to pay for the whole term (1 year or 3years) which is not cost effective

https://aws.amazon.com/eb s/features/

"Provisioned IOPS volumes are backed by solid-state drives (SSDs) and are the highest performance EBS volumes designed for your critical, I/O intensive database applications.

These volumes are ideal for both IOPS-intensive and throughput-intensive workloads that require extremely low latency."

https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/CHAP_Storage.html

To meet the requirements of the company to have access to both AWS and on-premises file storage with minimum latency, a hybrid cloud architecture can be used. One solution is to deploy and configure Amazon FSx for Windows File Server on AWS, which provides fully managed Windows file servers. The on-premises file data can be moved to the FSx File Gateway, which can act as a bridge between on-premises and AWS file storage. The cloud workloads can be configured to use FSx for Windows File Server on AWS, while the on-premises workloads can be configured to use the FSx File Gateway. This solution minimizes operational overhead and requires no significant changes to the existing file access patterns. The connectivity between on-premises and AWS can be established using an AWS Site-to-Site VPN connection.

AURORA is 5x performance improvement over MySQL on RDS and handles more read requests than write,; maintaining high availability = Multi-AZ deployment

https://docs.aws.amazon.com/filegateway/latest/filefsxw/what-is-file-fsxw.html

To meet the requirements of the company to have access to both AWS and on-premises file storage with minimum latency, a hybrid cloud architecture can be used. One solution is to deploy and configure Amazon FSx for Windows File Server on AWS, which provides fully managed Windows file servers. The on-premises file data can be moved to the FSx File Gateway, which can act as a bridge between on-premises and AWS file storage. The cloud workloads can be configured to use FSx for Windows File Server on AWS, while the on-premises workloads can be configured to use the FSx File Gateway. This solution minimizes operational overhead and requires no significant changes to the existing file access patterns. The connectivity between on-premises and AWS can be established using an AWS Site-to-Site VPN connection.

https://aws.amazon.com/sqs/features/

By routing incoming requests to Amazon SQS, the company can decouple the job requests from the processing instances. This allows them to scale the number of instances based on the size of the queue, providing more resources when needed. Additionally, using an Auto Scaling group based on the queue size will automatically scale the number of instances up or down depending on the workload. Updating the software to read from the queue will allow it to process the job requests in a more efficient manner, improving the performance of the system.

 AWS Snowball Edge is a type of Snowball device with on-board storage and compute power for select AWS capabilities. Snowball Edge can do local processing and edge-computing workloads in addition to transferring data between your local environment and the AWS Cloud1. Users can order an AWS Snowball Edge Storage Optimized device that includes Amazon EC2 compute to move 50 TB of data from on premises to AWS. The Storage Optimized device has 80 TB of usable storage and 40 vCPUs of compute power2. Users can copy the data to the device using the AWS OpsHub graphical user interface or the Snowball client command line tool3. Users can also create and run Amazon EC2 instances on the device using Amazon Machine Images (AMIs) that are compatible with the sbe1 instance type. Users can use the Snowball Edge device to transfer the data and run the transformation job locally without using any network bandwidth.

Users can also create a new EC2 instance on AWS to run the transformation application after the data transfer is complete. Amazon EC2 is a web service that provides secure, resizable compute capacity in the cloud. Users can launch an EC2 instance in the same AWS Region where they send their Snowball Edge device and choose an AMI that matches their application requirements. Users can use the EC2 instance to continue running the transformation job in the AWS Cloud.

"The Object Lock legal hold operation enables you to place a legal hold on an object version. Like setting a retention period, a legal hold prevents an object version from being overwritten or deleted. However, a legal hold doesn't have an associated retention period and remains in effect until removed." https://docs.aws.amazon.com/AmazonS3/latest/userguide/batch-ops-legal-hold.html

In this scenario, the company wants to avoid regional data transfer charges while downloading and uploading images from Amazon S3. To accomplish this at the lowest cost, the NAT gateway should be launched in each availability zone that the EC2 instances are running in. This allows the EC2 instances to route traffic through the local NAT gateway instead of sending traffic across an availability zone boundary and incurring regional data transfer fees. This method will help reduce the data transfer costs since inter-Availability Zone data transfers in a single region are free of charge.

The correct answer is Option D. Deploy an S3 VPC gateway endpoint into the VPC and attach an endpoint policy that allows access to the S3 buckets. By deploying an S3 VPC gateway endpoint, the application can access the S3 buckets over a private network connection within the VPC, eliminating the need for data transfer over the internet. This can help reduce data transfer fees as well as improve the performance of the application. The endpoint policy can be used to specify which S3 buckets the application has access to.

Point-in-time recovery (PITR) for DynamoDB is a feature that enables you to restore your table data to any point in time during the last 35 days. PITR helps protect your table from accidental write or delete operations, such as a test script writing to a production table or a user issuing a wrong command. PITR is easy to use, fully managed, fast, and scalable. You can enable PITR with a single click in the DynamoDB console or with a simple API call. You can restore a table to a new table using the console, the AWS CLI, or the DynamoDB API. PITR does not consume any provisioned table capacity and has no impact on the performance or availability of your production applications. PITR meets the requirements of the company with the least operational overhead, as it does not require any manual backup creation, scheduling, or maintenance. It also provides per-second granularity for restoring the table to any point within the last 24 hours.

References:

Point-in-time recovery for DynamoDB - Amazon DynamoDB

Amazon DynamoDB point-in-time recovery (PITR)

Enable Point-in-Time Recovery (PITR) for Dynamodb global tables

Restoring a DynamoDB table to a point in time - Amazon DynamoDB

Point-in-time recovery: How it works - Amazon DynamoDB

AWS Secrets Manager helps you protect secrets needed to access your applications, services, and IT resources. The service enables you to easily rotate, manage, and retrieve database credentials, API keys, and other secrets throughout their lifecycle.

https://docs.aws.amazon.com/secretsmanager/latest/userguide/intro.html

Secrets Manager enables you to replace hardcoded credentials in your code, including passwords, with an API call to Secrets Manager to retrieve the secret programmatically. This helps ensure the secret can't be compromised by someone examining your code, because the secret no longer exists in the code. Also, you can configure Secrets Manager to automatically rotate the secret for you according to a specified schedule. This enables you to replace long-term secrets with short-term ones, significantly reducing the risk of compromise.

Creating an Amazon Simple Queue Service (SQS) queue and configuring the S3 bucket to send a notification to the SQS queue when an image is uploaded to the S3 bucket will ensure that the Lambda function is triggered in a stateless and durable manner.

Configuring the Lambda function to use the SQS queue as the invocation source, and deleting the message in the queue after it is successfully processed will ensure that the Lambda function processes the image in a stateless and durable manner.

Amazon SQS is a fully managed message queuing service that enables you to decouple and scale microservices, distributed systems, and serverless applications. SQS eliminates the complexity and overhead associated with managing and operating-message oriented middleware, and empowers developers to focus on differentiating work. When new images are uploaded to the S3 bucket, SQS will trigger the Lambda function to process the image and compress it. Once the image is processed, the SQS message is deleted, ensuring that the Lambda function is stateless and durable.

"In some cases, this connection alone is not enough. It is always better to guarantee a fallback connection as the backup of DX. There are several options, but implementing it with an AWS Site-To-Site VPN is a real cost-effective solution that can be exploited to reduce costs or, in the meantime, wait for the setup of a second DX."

https://www.proud2becloud.com/hybrid-cloud-networking-backup-aws-direct-connect-network-connection-with-aws-site-to-site-vpn/

https://www.learnaws.org/2020/12/13/aws-rds-proxy-deep-dive/

RDS proxy can improve application availability in such a situation by waiting for the new database instance to be functional and maintaining any requests received from the application during this time. The end result is that the application is more resilient to issues with the underlying database.

This will enable solution to hold data till the time DB comes back to normal. RDS proxy is to optimally utilize the connection between Lambda and DB. Lambda can open multiple connection concurrently which can be taxing on DB compute resources, hence RDS proxy was introduced to manage and leverage these connections efficiently.

To achieve high availability with minimum downtime and minimum loss of data, the Auto Scaling group should be configured to use multiple Availability Zones to ensure that there is no single point of failure. The database should be configured as Multi-AZ to enable automatic failover in case of an outage in the primary Availability Zone. Additionally, an Amazon RDS Proxy instance can be used to improve the scalability and availability of the database by reducing connection failures and improving failover times.

To provide single sign-on (SSO) across all the company's accounts while continuing to manage users and groups in its on-premises self-managed Microsoft Active Directory, the solution is to enable AWS Single Sign-On (SSO) from the AWS SSO console and create a one-way forest trust or a one-way domain trust to connect the company's self-managed Microsoft Active Directory with AWS SSO by using AWS Directory Service for Microsoft Active Directory. This solution is described in the AWS documentation

Application availability: NLB cannot assure the availability of the application. This is because it bases its decisions solely on network and TCP-layer variables and has no awareness of the application at all. Generally, NLB determines availability based on the ability of a server to respond to ICMP ping or to correctly complete the three-way TCP handshake. ALB goes much deeper and is capable of determining availability based on not only a successful HTTP GET of a particular page but also the verification that the content is as was expected based on the input parameters.

Amazon Athena can be used to query JSON in S3

https://aws.amazon.com/kinesis/data- firehose/features/?nc=sn&loc=2#:~:text=into%20Amazon%20S3%2C%20Amazon%20Redshift%2C%20Amazon%20OpenSearch%20Service%2C%20Kinesis,Delivery%20streams

https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/PointInTimeRecovery.html

To ensure all Amazon EC2 instances, Amazon RDS DB instances, and Amazon Redshift clusters are configured with tags, a solutions architect should use AWS Config rules to define and detect resources that are not properly tagged. AWS Config rules are a set of customizable rules that AWS Config uses to evaluate AWS resource configurations for compliance with best practices and company policies. Using AWS Config rules can minimize the effort of configuring and operating this check because it automates the process of identifying non-compliant resources and notifying the responsible teams.

AWS Cost Explorer is a tool that enables you to view and analyze your costs and usage. You can explore your usage and costs using the main graph, the Cost Explorer cost and usage reports, or the Cost Explorer RI reports. You can view data for up to the last 12 months, forecast how much you're likely to spend for the next 12 months, and get recommendations for what Reserved Instances to purchase. You can use Cost Explorer to identify areas that need further inquiry and see trends that you can use to understand your costs. https://docs.aws.amazon.com/cost- management/latest/userguide/ce-what-is.html

This is the purpose of bookmarks: "AWS Glue tracks data that has already been processed during a previous run of an ETL job by persisting state information from the job run. This persisted state information is called a job bookmark. Job bookmarks help AWS Glue maintain state information and prevent the reprocessing of old data." https://docs.aws.amazon.com/glue/latest/dg/monitor-continuations.html

These are some of the main use cases for AWS DataSync: • Data migration – Move active datasets rapidly over the network into Amazon S3, Amazon EFS, or FSx for Windows File Server. DataSync includes automatic encryption and data integrity validation to help make sure that your data arrives securely, intact, and ready to use.

"DataSync includes encryption and integrity validation to help make sure your data arrives securely, intact, and ready to use." https://aws.amazon.com/datasync/faqs/

https://aws.amazon.com/premiumsupport/knowledge-center/s3-private-connection-no-authentication/

Static content can be cached at Cloud front Edge locations from S3 and dynamic content EC2 behind the ALB whose performance can be improved by Global Accelerator whose one endpoint is ALB and other Cloud front. So with regards to custom domain name endpoint is web application is R53 alias records for the custom domain point to web application https://aws.amazon.com/blogs/networking-and-content-d elivery/improving-availability-and-performance-for-application-load-balancers-using-one-click-integration-with-aws-global-accelerator/

The best option to improve the security of the data in transit is to configure a TLS listener and deploy the server certificate on the NLB. This will ensure that the data is encrypted and secure as it travels through the network. Additionally, you could also configure AWS Shield Advanced and enable AWS WAF on the NLB to further protect the network from malicious attacks. Alternatively, you could also change the load balancer to an Application Load Balancer (ALB) and enable AWS WAF on the ALB. Finally, you could also encrypt the Amazon Elastic Block Store (Amazon EBS) volume on the EC2 instances by using AWS Key Management Service (AWS KMS).

You must specify an SSL certificate for a TLS listener. The load balancer uses the certificate to terminate the connection and decrypt requests from clients before routing them to targets. https://docs.aws.amazon.com/elasticloadbalancing/latest/network/create-listener.html

To launch a one-deal-a-day website on AWS with millisecond latency during peak hours and with the least operational overhead, the best option is to use an Amazon S3 bucket to host the website's static content, deploy an Amazon CloudFront distribution, set the S3 bucket as the origin, use Amazon API Gateway and AWS Lambda functions for the backend APIs, and store the data in Amazon DynamoDB. This option requires minimal operational overhead and can handle millions of requests each hour with millisecond latency during peak hours. Therefore, option D is the correct answer.

https://aws.amazon.com/cn/blogs/security/how-to-connect-to-aws-secre ts-manager-service-within-a-virtual-private-cloud/

https://aws.amazon.com/blogs/security/rotate-amazon-rds-database-creden tials-automatically-with-aws-secrets-manager/

AWS Shield is a managed service that provides protection against Distributed Denial of Service (DDoS) attacks for applications running on AWS. AWS Shield Standard is automatically enabled to all AWS customers at no additional cost. AWS Shield Advanced is an optional paid service. AWS Shield Advanced provides additional protections against more sophisticated and larger attacks for your applications running on Amazon Elastic Compute Cloud (EC2), Elastic Load Balancing (ELB), Amazon CloudFront, AWS Global Accelerator, and Route 53.

https://docs.aws.amazon.com/w af/latest/developerguide/ddos-event-mitigation-logic-gax.html

https://aws.amazon.com/blogs/networking-and-content-delivery/adding-http-security-headers-using-lambdaedge-and-amazon-clo udfront/

Amazon CloudFront is a global content delivery network (CDN) service that can securely deliver web content, videos, and APIs at scale. It integrates with Cognito for authentication and with Lambda@Edge for authorization, making it an ideal choice for serving web content globally. Lambda@Edge is a service that lets you run AWS Lambda functions globally closer to users, providing lower latency and faster response times. It can also handle authorization logic at the edge to secure content in CloudFront. For this scenario, Lambda@Edge can provide authorization for the web application while leveraging the low-latency benefit of running at the edge.

as the policy prevents anyone from doing any EC2 action on any region except us-east-1 and allows only users with source ip 10.100.100.0/24 to terminate instances. So user with source ip 10.100.100.254 can terminate instances in us-east-1 region.

The basic difference between Snowball and Snowball Edge is the capacity they provide. Snowball provides a total of 50 TB or 80 TB, out of which 42 TB or 72 TB is available, while Amazon Snowball Edge provides 100 TB, out of which 83 TB is available.

To clone the production data into the test environment with high I/O performance and without affecting the production environment, the best option is to take EBS snapshots of the production EBS volumes and restore them onto new EBS volumes in the test environment. Then, attach the new EBS volumes to EC2 instances in the test environment. This option minimizes the time required to clone the data and ensures that modifications to the cloned data do not affect the production environment. Therefore, option C is the correct answer.

This answer is correct because it provides a resilient and durable replacement for the on-premises file share that is compatible with a serverless web application. Amazon S3 is a fully managed object storage service that can store any amount of data and serve it over the internet. It supports the following features:

Resilience: Amazon S3 stores data across multiple Availability Zones within a Region, and offers 99.999999999% (11 9’s) of durability. It also supports cross-region replication, which enables automatic and asynchronous copying of objects across buckets in different AWS Regions.

Durability: Amazon S3 encrypts data at rest using server-side encryption with either Amazon S3-managed keys (SSE-S3), AWS KMS keys (SSE-KMS), or customer-provided keys (SSE-C). It also supports encryption in transit using SSL/TLS. Amazon S3 also provides data protection features such as versioning, which keeps multiple versions of an object in the same bucket, and MFA Delete, which requires additional authentication for deleting an object version or changing the versioning state of a bucket.

Performance: Amazon S3 delivers high performance and scalability for serving static and dynamic web content. It also supports features such as S3 Transfer Acceleration, which speeds up data transfers by routing requests to AWS edge locations, and S3 Select, which enables retrieving only a subset of data from an object by using simple SQL expressions.

The S3 Standard-Infrequent Access (S3 Standard-IA) storage class is suitable for storing images that are rarely accessed, but must be immediately available when needed. It offers the same high durability, throughput, and low latency as S3 Standard, but with a lower storage cost per GB and a higher per-request cost.

References:

Amazon Simple Storage Service

Storage classes - Amazon Simple Storage Service

AWS Snowball is a secure data transport solution that accelerates moving large amounts of data into and out of the AWS cloud. It can move up to 80 TB of data at a time, and provides a network bandwidth of up to 50 Mbps, so it is well-suited for the task. Additionally, it is secure and easy to use, making it the ideal solution for this migration.

To meet the company's requirements of having a resilient solution that can process orders automatically in case of a system outage, the solutions architect needs to implement a fault-tolerant architecture. Based on the given scenario, a potential solution is to move the EC2 instances into an Auto Scaling group and configure the order system to send messages to an Amazon Simple Queue Service (Amazon SQS) queue. The EC2 instances can then consume messages from the queue.