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

Share the existing KMS key with the MSP external account because it has already been used to encrypt the AMI snapshot. https://docs.aws.amazon.com/kms/latest/developerguide/key-policy-modifying-external-accounts.html

To meet the requirements of detecting and alerting the administrators when PII is shared and automating remediation with the least development effort, the best approach would be to use Amazon S3 bucket as a secure transfer point and scan the objects in the bucket with Amazon Macie. 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 stored in Amazon S3. It can be used to classify sensitive data, monitor access to sensitive data, and automate remediation actions.

In this scenario, after uploading the files to the Amazon S3 bucket, the objects can be scanned for PII by Amazon Macie, and if it detects any PII, it can trigger an Amazon Simple Notification Service (SNS) notification to alert the administrators to remove the objects containing PII. This approach requires the least development effort, as Amazon Macie already has pre-built data classification rules that can detect PII in various formats.

Hence, option B is the correct answer.

References:

Amazon Macie User Guide: https://docs.aws.amazon.com/macie/latest/userguide/what-is-macie.html

AWS Well-Architected Framework - Security Pillar: https://docs.aws.amazon.com/wellarchitected/latest/security-pillar/welcome.html

"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

To reduce the cost of running the tests without reducing the compute and memory attributes of the Amazon RDS for MySQL DB instance, the development team can stop the instance when tests are completed and restart it when required. Stopping the DB instance when not in use can help save costs because customers are only charged for storage while the DB instance is stopped. During this time, automated backups and automated DB instance maintenance are suspended. When the instance is restarted, it retains the same configurations, security groups, and DB parameter groups as when it was stopped.

To meet the requirements of immediately accessible records for 1 year and then archived for an additional 9 years with maximum resiliency, we can use S3 Lifecycle policy to transition records from S3 Standard to S3 Glacier Deep Archive after 1 year. And to ensure that the records cannot be deleted by anyone, including administrative and root users, we can use S3 Object Lock in compliance mode for a period of 10 years. Therefore, the correct answer is option C.

To ensure that orders are processed in the order that they are received, the best solution is to use an Amazon SQS FIFO (First-In-First-Out) queue. This type of queue maintains the exact order in which messages are sent and received. In this case, the application can send information about new orders to an Amazon API Gateway REST API, which can then use an API Gateway integration to send a message to an Amazon SQS FIFO queue for processing. The queue can then be configured to invoke an AWS Lambda function to perform the necessary processing on each order. This ensures that orders are processed in the exact order in which they are received.

Amazon QuickSight is a data visualization service that allows you to create interactive dashboards and reports from various data sources, including Amazon S3 and Amazon RDS for PostgreSQL. You can connect all the data sources and create new datasets in QuickSight, and then publish dashboards to visualize the data. You can also share the dashboards with the appropriate users and groups, and control their access levels using IAM roles and permissions.

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.

AWS DataSync is an online data movement and discovery service that simplifies data migration and helps users quickly, easily, and securely move their file or object data to, from, and between AWS storage services1. Users can use AWS DataSync to transfer data between on-premises and AWS storage services. To use AWS DataSync, users need to install an AWS DataSync agent in the on-premises data center. The agent is a software appliance that connects to the source or destination storage system and handles the data transfer to or from AWS over the network2. Users also need to use AWS DataSync to create a suitable location configuration for the on-premises SFTP server. A location is a logical representation of a storage system that contains files or objects that users want to transfer using DataSync. Users can create locations for NFS shares, SMB shares, HDFS file systems, self-managed object storage, Amazon S3 buckets, Amazon EFS file systems, Amazon FSx for Windows File Server file systems, Amazon FSx for Lustre file systems, Amazon FSx for OpenZFS file systems, Amazon FSx for NetApp ONTAP file systems, and AWS Snowcone devices3.

The correct solution is to create an Object Lambda Access Point and an AWS Lambda function that redacts the PII when the function reads the file. This way, the company can use the S3 Object Lambda feature to modify the S3 object content on the fly, without creating a copy or changing the original object. The external service provider can access the Object Lambda Access Point and get the redacted version of the file. This solution has the least operational overhead because it does not require any additional storage, processing, or synchronization. The solution also scales automatically with the number of customer conversations and the demand from the external service provider. The other options are incorrect because:

Option B is using a batch process on an EC2 instance to read, redact, and write the files to a different S3 bucket. This solution has more operational overhead because it requires managing the EC2 instance, the batch process, and the additional S3 bucket. It also introduces latency and inconsistency between the original and the redacted files.

Option C is using a web application on an EC2 instance to present, redact, and download the files. This solution has more operational overhead because it requires managing the EC2 instance, the web application, and the download process. It also exposes the original files to the web application, which increases the risk of leaking the PII.

Option D is using a DynamoDB table and a Lambda function to store the non-PII data from the files. This solution has more operational overhead because it requires managing the DynamoDB table, the Lambda function, and the data transformation. It also changes the format and the structure of the original files, which may affect the quality control process.

References:

S3 Object Lambda

Object Lambda Access Point

Lambda function

https://docs.aws.amazon.com/rekognition/latest/dg/moderation.html?pg=ln &sec=ft

https://docs.aws.amazon.com/rekognition/latest/dg/a2i-rekognition.html

In Static Websites, Web pages are returned by the server which are prebuilt.

They use simple languages such as HTML, CSS, or JavaScript.

There is no processing of content on the server (according to the user) in Static Websites. Web pages are returned by the server with no change therefore, static Websites are fast.

There is no interaction with databases.

Also, they are less costly as the host does not need to support server-side processing with different languages.

============

In Dynamic Websites, Web pages are returned by the server which are processed during runtime means they are not prebuilt web pages but they are built during runtime according to the user’s demand.

These use server-side scripting languages such as PHP, Node.js, ASP.NET and many more supported by the server.

So, they are slower than static websites but updates and interaction with databases are possible.

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/

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.

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

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.

https://docs.aws.amazon.com/AWSEC2/latest/WindowsGuide/AmazonEFS.html Amazon FSx for Windows File Server provides fully managed Microsoft Windows file servers, backed by a fully native Windows file system. https://docs.aws.amazon.com/fsx/latest/WindowsGuide/what-is.html

https://aws.amazon.com/solutions/implementations/aws-streaming-data-solution-for-amazon-kinesis/

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

Understanding the Requirement: The company wants to improve caching to enhance website performance while ensuring that stale content is not served for more than a few minutes after a deployment.

Analysis of Options:

Set CloudFront TTL: Setting a short TTL (e.g., 2 minutes) ensures that cached content is refreshed frequently, reducing the risk of serving stale content.

S3 Bucket TTL: This would not control the cache duration for the CloudFront distribution.

Cache-Control Private: This directive is for controlling caching by private caches (e.g., browsers) and is not applicable for CloudFront.

Lambda@Edge: While this can add headers dynamically, it adds complexity and operational overhead.

Cache-Control max-age and CloudFront Invalidation: Setting a longer max-age for objects ensures they are cached longer, reducing load on the origin. Invalidation ensures that updated content is refreshed immediately after deployment.

Best Combination of Caching Methods:

Set the CloudFront default TTL to 2 minutes: This balances caching and freshness of content.

Add a Cache-Control max-age directive of 24 hours and use CloudFront invalidation: This ensures efficient caching while providing a mechanism to clear outdated content immediately after a deployment.

References:

Amazon CloudFront Caching

Invalidating Files in CloudFront

Amazon S3 is a highly scalable and durable object storage service that can store and retrieve any amount of data from anywhere on the web1. Users can configure the application to upload images directly from each user’s browser to Amazon S3 through the use of a presigned URL. A presigned URL is a URL that gives access to an object in an S3 bucket for a limited time and with a specific action, such as uploading an object2. Users can generate a presigned URL programmatically using the AWS SDKs or AWS CLI. By using a presigned URL, users can reduce coupling within the application and improve website performance, as they do not need to send the images to the web server first.

AWS Lambda is a serverless compute service that runs code in response to events and automatically manages the underlying compute resources3. Users can configure S3 Event Notifications to invoke an AWS Lambda function when an image is uploaded. S3 Event Notifications is a feature that allows users to receive notifications when certain events happen in an S3 bucket, such as object creation or deletion. Users can configure S3 Event Notifications to invoke a Lambda function that resizes the image and stores it back in the same or a different S3 bucket. This way, users can offload the image resizing task from the web server to Lambda.

https://aws.amazon.com/premiumsupport/knowledge-center/ec2-instance-access-s3-bucket/

Amazon CloudFront is a content delivery network (CDN) that caches content at edge locations around the world, providing low latency and high transfer speeds to users accessing the content. Adding a CloudFront distribution in front of the S3 bucket will cache the static website's content at edge locations around the world, decreasing latency for users accessing the website. This solution is also cost-effective as it only charges for the data transfer and requests made by users accessing the content from the CloudFront edge locations. Additionally, this solution provides scalability and reliability benefits as CloudFront can automatically scale to handle increased demand and provide high availability for the website.

Amazon S3 is a highly scalable and durable object storage service that can store and retrieve any amount of data from anywhere on the web1. Users can configure the application to upload images directly from each user’s browser to Amazon S3 through the use of a presigned URL. A presigned URL is a URL that gives access to an object in an S3 bucket for a limited time and with a specific action, such as uploading an object2. Users can generate a presigned URL programmatically using the AWS SDKs or AWS CLI. By using a presigned URL, users can reduce coupling within the application and improve website performance, as they do not need to send the images to the web server first.

AWS Lambda is a serverless compute service that runs code in response to events and automatically manages the underlying compute resources3. Users can configure S3 Event Notifications to invoke an AWS Lambda function when an image is uploaded. S3 Event Notifications is a feature that allows users to receive notifications when certain events happen in an S3 bucket, such as object creation or deletion. Users can configure S3 Event Notifications to invoke a Lambda function that resizes the image and stores it back in the same or a different S3 bucket. This way, users can offload the image resizing task from the web server to Lambda.

Moving the catalog to an Amazon Elastic File System (Amazon EFS) file system provides both high availability and durability. Amazon EFS is a fully-managed, highly-available, and durable file system that is built to scale on demand. With Amazon EFS, the catalog data can be stored and accessed from multiple EC2 instances in different availability zones, ensuring high availability. Also, Amazon EFS automatically stores files redundantly within and across multiple availability zones, making it a durable storage option.

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

This solution is the most suitable for running cron jobs on AWS with minimal refactoring, while also supporting the possibility of running jobs in response to future events.

Container Image for Cron Jobs: By containerizing the cron jobs, you can package the environment and dependencies required to run the jobs, ensuring consistency and ease of deployment across different environments.

Amazon EventBridge Scheduler: EventBridge Scheduler allows you to create a recurring schedule that can trigger tasks (like running your cron jobs) at specific times or intervals. It provides fine-grained control over scheduling and integrates seamlessly with AWS services.

AWS Fargate: Fargate is a serverless compute engine for containers that removes the need to manage EC2 instances. It allows you to run containers without worrying about the underlying infrastructure. Fargate is ideal for running jobs that can vary in duration, like cron jobs, as it scales automatically based on the task's requirements.

Why Not Other Options?:

Option A (Lambda): While AWS Lambda could handle short-running cron jobs, it has limitations in terms of execution duration (maximum of 15 minutes) and might not be suitable for jobs that run up to 20 minutes.

Option B (AWS Batch on ECS): AWS Batch is more suitable for batch processing and workloads that require complex job dependencies or orchestration, which might be more than what is needed for simple cron jobs.

Option D (Step Functions with Wait State): While Step Functions provide orchestration capabilities, this approach would introduce unnecessary complexity and overhead compared to the straightforward scheduling with EventBridge and running on Fargate.

AWS References:

Amazon EventBridge Scheduler - Details on how to schedule tasks using Amazon EventBridge Scheduler.

AWS Fargate - Information on how to run containers in a serverless manner using AWS Fargate.

Amazon RDS Custom for Oracle: This service allows you to bring your own Oracle Database licenses and provides the flexibility to customize the database settings, making it suitable for applications that require privileged access and third-party database features.

BYOL (Bring Your Own License):

RDS Custom supports the BYOL model, allowing you to use your existing Oracle licenses and comply with licensing requirements.

This helps in leveraging existing investments and reducing migration costs.

Customization and Third-Party Features:

RDS Custom allows for deeper customization of the database environment compared to standard RDS instances.

This makes it possible to support the third-party features that your application relies on without significant changes.

Migration Process:

Use native Oracle tools like Data Pump or RMAN to migrate the database to RDS Custom.

Customize the database settings post-migration to ensure compatibility with third-party features.

References:

Amazon RDS Custom for Oracle

Migrating to Amazon RDS Custom

To securely integrate Amazon S3 with an application that uses Amazon Cognito for user authentication, the following two steps are essential:

Detailed Explanation:

Step 1: Create an Amazon Cognito Identity Pool (Option A)

Amazon Cognito Identity Pools allow users to obtain temporary AWS credentials to access AWS resources, such as Amazon S3, after successfully authenticating with the Cognito user pool. The identity pool bridges the gap between user authentication and AWS service access by generating temporary credentials using AWS Identity and Access Management (IAM).

Once a user logs in using the Cognito User Pool, the identity pool provides IAM roles with specific permissions that the application can use to access S3 securely. This ensures that each user has appropriate access controls while accessing the S3 bucket.

This is a secure way to ensure that users only have temporary and least-privilege access to the S3 bucket for their documents.

Step 2: Create an Amazon S3 VPC Endpoint (Option C)

By creating an Amazon S3 VPC endpoint, the company ensures that communication between the application (which is hosted in a private subnet) and the S3 bucket occurs over the AWS private network, without the need to traverse the internet. This enhances security and prevents exposure of data to public networks.

The VPC endpoint allows the application to access the S3 bucket privately and securely within the VPC. It also ensures that traffic stays within the AWS network, reducing attack surface and improving overall security.

Why the Other Options Are Incorrect:

Option B: This is incorrect because Amazon Cognito User Pools are used for user authentication, not for generating S3 access tokens. To provide S3 access, you need to use Amazon Cognito Identity Pools, which offer AWS credentials.

Option D: A NAT gateway is unnecessary in this scenario. Using a VPC endpoint for S3 access provides a more secure and cost-effective solution by keeping traffic within AWS.

Option E: Attaching a policy to restrict access based on IP addresses is not scalable or efficient. It would require managing users’ dynamic IP addresses, which is not an effective security measure for this use case.

AWS References:

Amazon Cognito Identity Pools

Amazon VPC Endpoints for S3

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.

Understanding the Requirement: The job is stateful, cannot tolerate interruptions, and needs to run reliably for at least one hour each week.

Analysis of Options:

AWS Fargate with Amazon ECS and EventBridge: This option provides a serverless compute engine for containers that can run stateful tasks reliably. Using EventBridge Scheduler, the job can be triggered automatically at the specified time without manual intervention.

AWS Lambda with EventBridge: Lambda functions are not suitable for long-running stateful jobs since they have a maximum execution time of 15 minutes.

EC2 Spot Instances: Spot Instances can be interrupted, making them unsuitable for a stateful job that cannot tolerate interruptions.

AWS DataSync: This service is primarily for moving large amounts of data and is not designed to run stateful analysis jobs.

Best Option for Reliable, Scheduled Execution:

The Fargate task on ECS with EventBridge Scheduler meets all requirements, providing the necessary reliability and scheduling capabilities without interruption risks.

References:

Amazon ECS

AWS Fargate

Amazon EventBridge

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

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??.

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

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

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 of providing encryption at both the network and session layers while also allowing for controlled access between on-premises systems and AWS resources.

AWS Site-to-Site VPN: This service allows you to establish a secure and encrypted connection between your on-premises network and AWS VPC over the internet or via AWS Direct Connect. The VPN encrypts data at the network layer (IPsec) as it travels between the corporate network and AWS.

Routing and Security Controls: By configuring route table entries, you can ensure that only the traffic intended for AWS resources is directed to the VPC. Additionally, by setting up security groups and network ACLs, you can further restrict and control which traffic is allowed to communicate with the instances within your VPC. This approach provides the necessary security to prevent unrestricted access, aligning with the company’s security policies.

Why Not Other Options?:

Option A (AWS Direct Connect): While Direct Connect provides a private connection, it does not inherently provide encryption. Additional steps would be required to encrypt traffic, and it doesn’t address the session layer encryption.

Option B (IAM policies for Console access): This option does not meet the requirement for network-level encryption and security between the corporate network and the VPC.

Option D (AWS Transit Gateway): Although Transit Gateway can help in managing multiple connections, it doesn’t directly provide encryption at the network layer. You would still need to configure a VPN or use other methods for encryption.

AWS References:

AWS Site-to-Site VPN - Overview of AWS Site-to-Site VPN capabilities, including encryption.

Security Groups and Network ACLs - Information on configuring security groups and network ACLs to control traffic.

Understanding the Requirement: The company has petabytes of data in S3 Standard across multiple buckets with varying access frequencies. They do not know the access patterns and need a cost-optimized storage solution with minimal operational effort.

Analysis of Options:

S3 Intelligent-Tiering: This storage class automatically moves data between two access tiers (frequent and infrequent) based on changing access patterns. It incurs a small monitoring and automation charge but eliminates the need to manually move data between storage classes.

S3 Storage Class Analysis Tool: While useful for determining access patterns, this tool requires manual intervention to move objects to the appropriate storage class, which increases operational overhead.

S3 Glacier Instant Retrieval: This storage class is designed for data that is rarely accessed but requires instant retrieval when needed. It may not be suitable for data with unknown and varying access patterns.

S3 One Zone-IA: This is a lower-cost option for infrequently accessed data stored in a single availability zone. It does not provide the same level of durability and availability as other options and requires knowledge of access patterns.

Best Option for Operational Efficiency:

S3 Intelligent-Tiering provides the best balance of cost savings and operational efficiency. It dynamically adjusts to access patterns without manual intervention, ensuring the company is only paying for what they need without the risk of incurring high costs for infrequent access data.

References:

Amazon S3 Intelligent-Tiering

Managing your storage lifecycle

This solution is the most cost-effective and scalable for analyzing large amounts of web traffic logs.

Amazon S3: Storing the logs in Amazon S3 is highly scalable, durable, and cost-effective. S3 is designed to handle large-scale data storage, which is ideal for storing tens of gigabytes of log data generated daily by multiple websites.

Amazon Athena: Athena is a serverless, interactive query service that allows you to analyze data in S3 using standard SQL. It works directly with the data stored in S3, so there’s no need to load the data into a database, which saves on costs and reduces complexity. Athena charges based on the amount of data scanned by queries, making it a cost-effective solution for on-demand analysis that only occurs once a week.

Why Not Other Options?:

Option B (Amazon RDS): Storing logs in a relational database like Amazon RDS would be more expensive due to the storage and I/O costs associated with RDS. Additionally, it would require more management overhead.

Option C (Amazon OpenSearch Service): OpenSearch is a good option for full-text search and analytics on log data, but it might be more costly and complex to manage compared to the simplicity and cost-effectiveness of Athena for periodic SQL-based queries.

Option D (Amazon EMR): While EMR can handle large-scale data processing, it involves more operational overhead and might be overkill for the type of ad-hoc, SQL-based analysis required here. Additionally, EMR costs can be higher due to the need to maintain a cluster.

AWS References:

Amazon S3 - Information on how to store and manage data in Amazon S3.

Amazon Athena - Documentation on using Amazon Athena for querying data stored in S3 using SQL.

Application Load Balancer (ALB): ALB is suitable for routing HTTP/HTTPS traffic to the application servers. It provides advanced routing features and integrates well with Auto Scaling groups.

Target Group Configuration:

Create a target group for the application servers and register the Auto Scaling group with this target group.

Configure the ALB to forward requests from the web servers to the application servers.

Security Group Setup:

Configure the security group of the application servers to only allow traffic from the web servers' security group.

This ensures that only the web servers can access the application servers, meeting the requirement to limit access.

Benefits:

Security: Using security groups to restrict access ensures a secure environment where only intended traffic is allowed.

Scalability: ALB works seamlessly with Auto Scaling groups, ensuring the application can handle varying loads efficiently.

References:

Application Load Balancer

Security Groups for Your VPC

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

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

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

AWS Glue Crawler: AWS Glue is a fully managed ETL (Extract, Transform, Load) service that makes it easy to prepare and load data for analytics. A Glue crawler can automatically discover new data and schema in Amazon S3, making it easy to keep the data catalog up-to-date.

Crawling the Data:

Set up an AWS Glue crawler to scan the S3 bucket containing the clickstream data.

The crawler will automatically detect the schema and create/update the tables in the AWS Glue Data Catalog.

Amazon Athena:

Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL.

Once the data catalog is updated by the Glue crawler, use Athena to query the clickstream data directly in S3.

Operational Efficiency: This solution leverages fully managed services, reducing operational overhead. Glue crawlers automate data cataloging, and Athena provides a serverless, pay-per-query model for quick data analysis without the need to set up or manage infrastructure.

References:

AWS Glue

Amazon Athena

Current Backup Settings: By default, Amazon RDS creates automated backups with a retention period of 7 days.

Regulatory Requirements: The requirement is to retain daily backups for 30 days.

Adjusting Retention Period: You can modify the RDS instance settings to increase the automated backup retention period to 30 days.

Operational Overhead: This solution is the simplest as it leverages existing automated backups and requires minimal intervention.

Implementation: The change can be made via the AWS Management Console, AWS CLI, or AWS SDKs.

References

Amazon RDS Backups: Amazon RDS Documentation

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

This solution is the most cost-effective and meets the RPO and RTO requirements of 24 hours.

Automatic Snapshots: Amazon RDS automatically creates snapshots of your DB instance at regular intervals. By copying these snapshots to another AWS Region every 24 hours, you ensure that you have a backup available in a different geographic location, providing disaster recovery capability.

RPO and RTO: Since the company’s RPO and RTO are both 24 hours, copying snapshots daily to another Region is sufficient. In the event of a disaster, you can restore the DB instance from the most recent snapshot in the target Region.

Why Not Other Options?:

Option A (Cross-Region Read Replica): This could provide a faster recovery time but is more costly due to the ongoing replication and resource usage in another Region.

Option B (DMS Cross-Region Replication): While effective for continuous replication, it introduces complexity and cost that isn’t necessary given the 24-hour RPO/RTO.

Option C (Cross-Region Native Backup Copy): This involves more manual steps and doesn’t offer as straightforward a solution as automated snapshot copying.

AWS References:

Amazon RDS Automated Backups and Snapshots - Details on automated backups and snapshots in RDS.

Copying an Amazon RDS DB Snapshot - How to copy DB snapshots to another Region.

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 Inspector:

• Performs active vulnerability scans of EC2 instances. It looks for software vulnerabilities, unintended network accessibility, and other security issues.

• Requires installing an agent on EC2 instances to perform scans. The agent must be deployed to each instance.

• Provides scheduled scan reports detailing any findings of security risks or vulnerabilities. These reports can be used to patch or remediate issues.

• Is best suited for proactively detecting security weaknesses and misconfigurations in your AWS environment.

Amazon FSx for Lustre: Lustre is a high-performance file system designed for workloads that require fast storage with sustained high throughput and low latency. It integrates with Amazon S3, making it suitable for HPC environments.

Persistent File Systems:

Persistent Storage: Suitable for long-term storage and recurrent use, providing durability and availability.

High Throughput and Low Latency: Persistent Lustre file systems can handle large amounts of data with sub-millisecond latency, meeting the needs of high-performance computing workloads.

Simultaneous Access: FSx for Lustre allows thousands of compute instances to access and process large datasets concurrently, ensuring that the high volume of data is handled efficiently.

Highly Available: FSx for Lustre is designed to provide high availability and is managed by AWS, reducing the operational burden.

References:

Amazon FSx for Lustre

High-Performance Computing on AWS

Amazon Route 53 Multivalue Answer Routing: This routing policy allows Route 53 to return multiple values, such as IP addresses, in response to DNS queries. This can distribute traffic across multiple resources and includes health checks to ensure traffic is only routed to healthy instances.

Health Checks:

Configure health checks for each Region to monitor the health of the website instances.

Route 53 will only include healthy instances in the DNS responses, ensuring that traffic is not routed to an unhealthy Region.

Load Distribution and Disaster Recovery:

Multivalue answer routing helps balance the load between instances in different Regions.

If instances in one Region become unhealthy, Route 53 will route traffic to the healthy instances in the other Region.

Operational Simplicity: This solution does not require complex configurations or additional resources, making it a simple yet effective way to distribute traffic and ensure high availability.

References:

Amazon Route 53 Routing Policies

Multivalue Answer Routing

In Amazon Aurora, a custom endpoint is a feature that allows you to create a load-balanced endpoint that directs traffic to a specific set of instances in your Aurora DB cluster. This is particularly useful when you want to route traffic to a subset of instances that have different configurations or when you want to isolate specific workloads (e.g., reporting queries) to certain instances.

Custom Endpoint: The correct solution is to create a custom endpoint that includes the three Aurora Replicas that the department wants to use for near-real-time reporting. This custom endpoint will distribute the reporting queries only across the three selected replicas with the specified compute and memory configurations, ensuring that these queries do not affect the rest of the DB cluster.

Other Options:

Option B (Create a three-node cluster clone): This would create a separate cluster with its own resources, but it is not necessary and could incur additional costs. Also, it doesn't leverage the existing replicas.

Option C (Use any of the instance endpoints): This would involve manually managing connections to individual instances, which is not scalable or automatic.

Option D (Use the reader endpoint): The reader endpoint would distribute the read queries across all replicas in the cluster, not just the selected three. This would not meet the requirement to limit the reporting queries to only three specific replicas.

AWS References:

Amazon Aurora Endpoints - Provides detailed information on the different types of endpoints available in Aurora, including custom endpoints.

Custom Endpoints in Amazon Aurora - Specific documentation on how to create and use custom endpoints to direct traffic to selected instances in an Aurora cluster.

Understanding the Requirement: The company needs to redesign the architecture to be highly available and use AWS managed solutions for hosting a web application with static content, PHP application, and Redis for user sessions.

Analysis of Options:

AWS Elastic Beanstalk: Suitable for simplifying deployment but may not provide the desired flexibility and control for complex architectures.

AWS Lambda and API Gateway: Not ideal for hosting a stateful PHP application and handling static content. Adding complexity without significant benefit.

EC2 instance with ElastiCache and S3: Provides some high availability but involves managing EC2 instances, which increases operational overhead.

CloudFront with S3, ALB, ECS with Fargate, and ElastiCache: This solution leverages fully managed AWS services for each component, ensuring high availability and scalability.

Best Solution:

CloudFront with S3, ALB, ECS with Fargate, and ElastiCache: This combination of services meets the requirements for a highly available and managed solution, ensuring optimal performance and minimal operational overhead.

References:

Amazon CloudFront

Amazon S3

Amazon ECS with Fargate

Amazon ElastiCache for Redis

EC2 Account Attributes: Amazon EC2 allows you to set account attributes to automatically encrypt new EBS volumes. This ensures that all new volumes created in your account are encrypted by default.

Configuration Steps:

Go to the EC2 Dashboard.

Select "Account Attributes" and then "EBS encryption".

Enable default EBS encryption and select the default AWS KMS key or a customer-managed key.

Prevention of Unencrypted Volumes: By setting this account attribute, you ensure that it is not possible to create unencrypted EBS volumes, thereby enforcing compliance with security requirements.

Operational Efficiency: This solution requires minimal configuration changes and provides automatic enforcement of encryption policies, reducing operational overhead.

References:

Amazon EC2 Default EBS Encryption

Amazon RDS Proxy: RDS Proxy is a fully managed, highly available database proxy that makes applications more resilient to database failures by pooling and sharing connections, and it can automatically handle database failovers.

Reduced Failover Time: By using RDS Proxy, the connection management between the application and the database is improved, reducing failover times significantly. RDS Proxy maintains connections in a connection pool and reduces the time required to re-establish connections during a failover.

Configuration:

Create an RDS Proxy instance.

Configure the proxy to connect to the RDS for PostgreSQL DB instance.

Modify the application configuration to use the RDS Proxy endpoint instead of the direct database endpoint.

Operational Benefits: This solution provides high availability and reduces application timeouts during failovers with minimal changes to the application code.

References:

Amazon RDS Proxy

Setting Up RDS Proxy

Amazon Athena is a serverless query service that allows you to run SQL queries directly on data stored in Amazon S3 without the need for a data warehouse. It is cost-effective because you only pay for the queries you run, and it can handle Apache Parquet files efficiently. Additionally, Athena integrates with KMS, making it suitable for querying encrypted data.

Key AWS features:

Cost-Effective: Athena charges only for the data scanned by the queries, making it a more cost-effective solution compared to Redshift or EMR for occasional queries.

Direct S3 Querying: Athena supports querying data directly in S3, including Parquet files, without needing to move the data.

AWS Documentation: Athena's compatibility with encrypted Parquet files in S3 makes it the ideal choice for this scenario, reducing both cost and complexity??.

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)

A gateway VPC endpoint for Amazon S3 enables private connections between the VPC and Amazon S3 that do not require an internet gateway or NAT device. This minimizes costs and prevents traffic from traversing the internet. A gateway VPC endpoint uses a prefix list as the route target in a VPC route table to route traffic privately to Amazon S31. Associating the endpoint with all route tables in the VPC ensures that all subnets can access Amazon S3 through the endpoint.

Option A is incorrect because S3 Intelligent-Tiering is a storage class that optimizes storage costs by automatically moving objects between two access tiers based on changing access patterns. It does not affect the network traffic between the VPC and Amazon S32.

Option B is incorrect because S3 Transfer Acceleration is a feature that enables fast, easy, and secure transfers of files over long distances between clients and an S3 bucket. It does not prevent traffic from traversing the internet3.

Option D is incorrect because an interface VPC endpoint for Amazon S3 is powered by AWS PrivateLink, which requires an elastic network interface (ENI) with a private IP address in each subnet. This adds complexity and cost to the solution. Moreover, an interface VPC endpoint does not support cross-Region access to Amazon S3. Reference URL: 1: https://docs.aws.amazon.com/vpc/latest/privatelink/vpc-endpoints-s3.html  2: https://docs.aws.amazon.com/AmazonS3/latest/userguide/storage-class-intro.html#sc-dynamic-data-access  3: https://docs.aws.amazon.com/AmazonS3/latest/userguide/transfer-acceleration.html : https://aws.amazon.com/blogs/architecture/choosing-your-vpc-endpoint-strategy-for-amazon-s3/

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

Requirement Analysis: The goal is to prevent accidental deletion of EBS snapshots while avoiding indefinite retention.

Recycle Bin for EBS Snapshots: AWS Recycle Bin allows for retention rules that prevent immediate deletion of snapshots, providing a safety net against accidental deletions.

Retention Rule: A 7-day retention rule ensures snapshots are not permanently deleted immediately, giving time to recover from accidental deletions.

Implementation:

Enable Recycle Bin in your AWS account.

Create a retention rule that specifies a 7-day period for EBS snapshots.

Apply this rule to all EBS snapshots.

Conclusion: This solution provides an automated way to prevent data loss from accidental deletions with minimal development effort.

References

AWS Recycle Bin: AWS Recycle Bin Documentation

 This solution meets the requirements with the least operational overhead because it uses AWS services that are fully managed and scalable. The EventBridge rule can detect the DeleteKey operation from the AWS KMS API and trigger the Systems Manager Automation runbook, which can execute a predefined workflow to cancel the key deletion. The EventBridge rule can also publish an SNS message to the topic that sends an email notification to the administrators. This way, the company can prevent the accidental deletion of KMS keys and notify the administrators of any attempts to delete them.

Option A is not a valid solution because AWS Config rules are used to evaluate the configuration of AWS resources, not to cancel the deletion of KMS keys. Option B is not a valid solution because it requires creating and maintaining a custom Lambda function that has logic to prevent KMS key deletion, which adds operational overhead. Option D is not a valid solution because it only notifies the administrators of the DeleteKey operation, but does not cancel it.

References:

Using Amazon EventBridge rules to trigger Systems Manager Automation workflows - AWS Systems Manager

Using Amazon SNS for system-to-administrator communications - Amazon Simple Notification Service

Deleting AWS KMS keys - AWS Key Management Service

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.

AWS Control Tower: Provides a managed service to set up and govern a secure, multi-account AWS environment based on AWS best practices. It automates the setup of AWS Organizations and applies security controls (guardrails).

Networking Account:

Create a centralized networking account that includes a VPC with both private and public subnets.

This centralized VPC will manage and control the networking resources.

AWS Resource Access Manager (AWS RAM):

Use AWS RAM to share the subnets from the networking account with the other workload accounts.

This allows different workload accounts to utilize the shared networking resources without the need to manage their own VPCs.

Operational Efficiency: Using AWS Control Tower simplifies the setup and governance of multiple AWS accounts, while AWS RAM facilitates centralized management of networking resources, reducing operational overhead and ensuring consistent security and compliance.

References:

AWS Control Tower

AWS Resource Access Manager

Understanding the Requirement: The application running in a private subnet needs to store and retrieve data from S3 without data traveling over the public internet.

Analysis of Options:

NAT Gateway: Allows private subnets to access the internet but incurs additional costs and still routes traffic through the public internet.

AWS Storage Gateway: Provides hybrid cloud storage solutions but is not the most cost-effective for direct S3 access from within the VPC.

S3 Interface Endpoint: Provides private access to S3 but is generally used for specific use cases where more granular control is required, which might be overkill and more expensive.

S3 Gateway Endpoint: Provides private, cost-effective access to S3 from within the VPC without routing traffic through the public internet.

Best Solution:

S3 Gateway Endpoint: This option meets the requirements for secure, private access to S3 from a private subnet most cost-effectively.

References:

Amazon VPC Endpoints

Gateway Endpoints

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.

Service control policies (SCPs) are one type of policy that you can use to manage 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. See https://docs.aws.amazon.com/organizations/latest/userguide/orgs_manage_policies_scp.html.

Use a group email address for the management account's root user https://docs.aws.amazon.com/organizations/latest/userguide/orgs_best-practices_mgmt-acct.html#best-practices_mgmt-acct_email-address

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

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

EC2 instance Savings Plan saves 72% while Compute Savings Plans saves 66%. But according to link, it says "Compute Savings Plans provide the most flexibility and help to reduce your costs by up to 66%. These plans automatically apply to EC2 instance usage regardless of instance family, size, AZ, region, OS or tenancy, and also apply to Fargate and Lambda usage." EC2 instance Savings Plans are not applied to Fargate or Lambda

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.

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

**AWS PrivateLink provides private connectivity between VPCs, AWS services, and your on-premises networks, without exposing your traffic to the public internet**. AWS PrivateLink makes it easy to connect services across different accounts and VPCs to significantly simplify your network architecture. Interface **VPC endpoints**, powered by AWS PrivateLink, connect you to services hosted by AWS Partners and supported solutions available in AWS Marketplace. https://aws.amazon.com/privatelink/

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

https://docs.aws.amazon.com/sns/latest/dg/sns-dead-letter-queues.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

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

This solution meets the requirements of saving money on storage while keeping the most accessed files readily available for the users. S3 Lifecycle policy can automatically move objects from one storage class to another based on predefined rules. S3 Standard-IA is a lower-cost storage class for data that is accessed less frequently, but requires rapid access when needed. It is suitable for ringtones older than 90 days that are downloaded infrequently.

Option A is incorrect because configuring S3 Standard-IA for the initial storage tier of the objects can incur higher costs for frequent access and retrieval fees. Option B is incorrect because moving the files to S3 Intelligent-Tiering can incur additional monitoring and automation fees that may not be necessary for ringtones older than 90 days. Option C is incorrect because using S3 inventory to manage objects and move them to S3 Standard-IA can be complex and time-consuming, and it does not provide automatic cost savings.

References:

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

https://aws.amazon.com/s3/cloud-storage-cost-optimization-ebook/

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

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

This solution meets the requirements of directing users to a backup static error page if the primary website is unavailable, minimizing changes and infrastructure overhead. Route 53 active-passive failover configuration can route traffic to a primary resource when it is healthy or to a secondary resource when the primary resource is unhealthy. Route 53 health checks can monitor the health of the ALB endpoint and trigger the failover when needed. The static error page can be hosted in an S3 bucket that is configured as a website, which is a simple and cost-effective way to serve static content.

Option A is incorrect because using a latency routing policy can route traffic based on the lowest network latency for users, but it does not provide failover functionality. Option C is incorrect because using an active-active configuration with the ALB and an EC2 instance can increase the infrastructure overhead and complexity, and it does not guarantee that the EC2 instance will always be healthy. Option D is incorrect because using a multivalue answer routing policy can return multiple values for a query, but it does not provide failover functionality.

References:

https://docs.aws.amazon.com/Route53/latest/DeveloperGuide/routing-policy-failover.html

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

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

Amazon S3 is cheapest and can be accessed from anywhere.

https://aws.amazon.com/p remiumsupport/knowledge-center/cloudwatch-memory-metrics-ec2/

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

https://docs.aws.amazon.com/AWSEC2/latest/WindowsGuide/monitor -ami-events.html#:~:text=For%20example%2C%20you%20can%20create%20an%20EventBridge%20rule%20that%20detects%20when%20the%20AMI%20creation%20process%20has%20completed%20and%20then%20invokes%20an%20Amazon%20SNS%20topic%20to%20send%20an%20email%20notification%20to%20you.

Creating an Amazon EventBridge (Amazon CloudWatch Events) rule for the CreateImage API call and configuring the target as an Amazon Simple Notification Service (Amazon SNS) topic to send an alert when a CreateImage API call is detected will meet the requirements with the least operational overhead. Amazon EventBridge is a serverless event bus that makes it easy to connect applications together using data from your own applications, integrated Software as a Service (SaaS) applications, and AWS services. By creating an EventBridge rule for the CreateImage API call, the company can set up alerts whenever this operation is called within their account. The alert can be sent to an SNS topic, which can then be configured to send notifications to the company's email or other desired destination.

https://aws.amazon.com/blogs/compute/building-loosely-coupled-scalable-c-applications- with-amazon-sqs-and-amazon-sns/

https://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSDeveloperGuide/sqs-dead-letter-queues.html

Many applications, including those built on modern serverless architectures, can have a large number of open connections to the database server and may open and close database connections at a high rate, exhausting database memory and compute resources. Amazon RDS Proxy allows applications to pool and share connections established with the database, improving database efficiency and application scalability. https://aws.amazon.com/id/rds/proxy/

ElastiCache, enhances the performance of web applications by quickly retrieving information from fully-managed in-memory data stores. It utilizes Memcached and Redis, and manages to considerably reduce the time your applications would, otherwise, take to read data from disk-based databases. Amazon CloudFront supports dynamic content from HTTP and WebSocket protocols, which are based on the Transmission Control Protocol (TCP) protocol. Common use cases include dynamic API calls, web pages and web applications, as well as an application's static files such as audio and images. It also supports on-demand media streaming over HTTP. AWS Global Accelerator supports both User Datagram Protocol (UDP) and TCP-based protocols. It is commonly used for non-HTTP use cases, such as gaming, IoT and voice over IP. It is also good for HTTP use cases that need static IP addresses or fast regional failover

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

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

https://aws.amazon.com/certificate-manager/ :

"With AWS Certificate Manager, you can quickly request a certificate, deploy it on ACM-integrated AWS resources, such as Elastic Load Balancers, Amazon CloudFront distributions, and APIs on API Gateway, and let AWS Certificate Manager handle certificate renewals. It also enables you to create private certificates for your internal resources and manage the certificate lifecycle centrally."

This approach ensures that the order creation and payment processing steps are separate and atomic. By sending the order information to an SQS FIFO queue, the payment service can process the order one at a time and in the order they were received. If the payment service is unable to process an order, it can be retried later, preventing the creation of multiple orders. The deletion of the message from the queue after it is processed will prevent the same message from being processed multiple times.

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"

(https://aws.amazon.com/blogs/security/how-to-restrict-amazon-s3-bucket-access-to-a-specific-iam-role/ )

https://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/field-level-encryption.html

"With Amazon CloudFront, you can enforce secure end-to-end connections to origin servers by using HTTPS. Field-level encryption adds an additional layer of security that lets you protect specific data throughout system processing so that only certain applications can see it."

By using an SQS queue and Lambda, the solutions architect can decouple the API front end from the processing microservices and improve the overall scalability and availability of the system. The SQS queue acts as a buffer, allowing the API front end to continue accepting user requests even if the processing microservices are experiencing high workloads or are temporarily unavailable. The Lambda function can then retrieve requests from the SQS queue and write them to DynamoDB, ensuring that all user requests are stored and processed. This approach allows the company to scale the processing microservices independently from the API front end, ensuring that the API remains available to users even during periods of high demand.

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/

This solution meets the requirements of creating digital copies for a large collection of historical written records, analyzing the documents, extracting the medical information, and storing the documents so that an application can run SQL queries on the data. Writing the document information to an Amazon S3 bucket can provide scalable and durable storage for the scanned files. Using Amazon Athena to query the data can provide serverless and interactive SQL analysis on data stored in S3. Creating an AWS Lambda function that runs when new documents are uploaded can provide event-driven and serverless processing of the scanned files. Using Amazon Textract to convert the documents to raw text can provide accurate optical character recognition (OCR) and extraction of structured data such as tables and forms from documents using artificial intelligence (AI). Using Amazon Comprehend Medical to detect and extract relevant medical information from the text can provide natural language processing (NLP) service that uses machine learning that has been pre-trained to understand and extract health data from medical text.

Option A is incorrect because writing the document information to an Amazon EC2 instance that runs a MySQL database can increase the infrastructure overhead and complexity, and it may not be able to handle large volumes of data. Option C is incorrect because creating an Auto Scaling group of Amazon EC2 instances to run a custom application that processes the scanned files and extracts the medical information can increase the infrastructure overhead and complexity, and it may not be able to leverage existing AI and NLP services such as Textract and Comprehend Medical. Option D is incorrect because using Amazon Rekognition to convert the documents to raw text can provide image and video analysis, but it does not support OCR or extraction of structured data from documents. Using Amazon Transcribe Medical to detect and extract relevant medical information from the text can provide speech-to-text transcription service for medical conversations, but it does not support text analysis or extraction of health data from medical text.

References:

https://aws.amazon.com/s3/

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

https://aws.amazon.com/lambda/

https://aws.amazon.com/textract/

https://aws.amazon.com/comprehend/medical/

This answer is correct because it reflects the effect of the IAM policy on the group members. The policy has two statements: one with an Allow effect and one with a Deny effect. The Allow statement grants permission to perform any EC2 action on any resource within the us-east-1 Region. The Deny statement overrides the Allow statement and denies permission to perform the ec2:StopInstances and ec2:TerminateInstances actions on any resource within the us-east-1 Region, unless the group member is logged in with MFA. Therefore, the group members can perform any EC2 action except stopping or terminating instances in the us-east-1 Region, unless they use MFA.

https://aws.amazo n.com/premiumsupport/knowledge-center/cloudfront-access-to-amazon-s3/

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

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.

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

We recommend that you use On-Demand Instances for applications with short-term, irregular workloads that cannot be interrupted. https://docs.aws.amazon.co m/AWSEC2/latest/UserGuide/ec2-on-demand-instances.html

Cloudfront for rapid response and s3 to minimize infrastructure.

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

Session Manager is a fully managed AWS Systems Manager capability. With Session Manager, you can manage your Amazon Elastic Compute Cloud (Amazon EC2) instances, edge devices, on-premises servers, and virtual machines (VMs). You can use either an interactive one-click browser-based shell or the AWS Command Line Interface (AWS CLI). Session Manager provides secure and auditable node management without the need to open inbound ports, maintain bastion hosts, or manage SSH keys. Session Manager also allows you to comply with corporate policies that require controlled access to managed nodes, strict security practices, and fully auditable logs with node access details, while providing end users with simple one-click cross-platform access to your managed nodes. https://docs.aws.amazon.com/systems-manager/latest/userguide/session-manager.html

This option is the most efficient because it uses data filters, which are specifications that restrict access to certain data in query results and engines integrated with Lake Formation1. Data filters can be used to implement row-level security and cell-level security, which are techniques to prevent access to portions of the data that contain sensitive information2. Data filters can be applied when granting Lake Formation permissions on a Data Catalog table, and can use PartiQL expressions to filter data based on conditions3. This solution meets the requirement of providing a secure solution to prevent access to portions of the data that contain sensitive information. Option A is less efficient because it uses an IAM role that includes permissions to access Lake Formation tables, which is a way to grant access to data in Lake Formation using IAM policies4. However, this does not provide a way to prevent access to portions of the data that contain sensitive information. Option C is less efficient because it uses an AWS Lambda function that removes sensitive information before Lake Formation ingests the data, which is a way to perform data cleansing or transformation using serverless functions. However, this could involve significant changes to the application code and logic, and could also result in data loss or inconsistency. Option D is less efficient because it uses an AWS Lambda function that periodically queries and removes sensitive information from Lake Formation tables, which is a way to perform data cleansing or transformation using serverless functions. However, this could involve significant changes to the application code and logic, and could also result in data loss or inconsistency.

https://docs.aws.amazon.com/prescriptive-guidance/latest/dynamodb-hierarchical-data-model/introduction.html

ElastiCache can help speed up the read performance of the database by caching frequently accessed data, reducing latency and allowing the application to access the data more quickly. This solution requires minimal modifications to the current architecture, as ElastiCache can be used in conjunction with the existing Amazon RDS for MySQL database.

"Sensitive data redaction replaces personally identifiable information (PII) in the text transcript and the audio file. A redacted transcript replaces the original text with [PII]; a redacted audio file replaces spoken personal information with silence. This parameter is useful for protecting customer information." https://docs.aws.amazon.com/transcribe/latest/dg/call-analytics-insights.html#call- analytics-insights-redaction

Many applications, including those built on modern serverless architectures, can have a large number of open connections to the database server and may open and close database connections at a high rate, exhausting database memory and compute resources. Amazon RDS Proxy allows applications to pool and share connections established with the database, improving database efficiency and application scalability. (https://aws.amazon.com/pt/rds/proxy/ )

This option is the most efficient because it uses AWS Key Management Service (AWS KMS), which is a service that makes it easy for you to create and manage cryptographic keys and control their use across a wide range of AWS services and with your applications running on AWS1. It also uses AWS KMS to encrypt the EBS volumes and Aurora database storage at rest, which provides data protection by encrypting your data with encryption keys that you manage23. It also uses AWS Certificate Manager (ACM), which is a service that lets you easily provision, manage, and deploy public and private Secure Sockets Layer/Transport Layer Security (SSL/TLS) certificates for use with AWS services and your internal connected resources. It also attaches an ACM certificate to the ALB to encrypt data in transit, which provides data protection by enabling SSL/TLS encryption for connections between clients and the load balancer. This solution meets the requirement of encrypting all data for the application at rest and in transit. Option A is less efficient because it uses AWS KMS certificates on the ALB to encrypt data in transit, which is not possible as AWS KMS does not provide certificates but only keys. It also uses AWS Certificate Manager (ACM) to encrypt the EBS volumes and Aurora database storage at rest, which is not possible as ACM does not provide encryption but only certificates. Option B is less efficient because it uses the AWS root account to log in to the AWS Management Console, which is not recommended as it has unrestricted access to all resources in your account. It also uploads the company’s encryption certificates, which is not necessary as ACM can provide certificates for free. It also selects the option to turn on encryption for all data at rest and in transit for the account, which is not possible as encryption settings are specific to each service and resource. Option D is less efficient because it uses BitLocker to encrypt all data at rest, which is a Windows feature that provides encryption for volumes on Windows servers. However, this does not provide encryption for Aurora database storage at rest, as Aurora runs on Linux servers. It also imports the company’s TLS certificate keys to AWS KMS, which is not necessary as ACM can provide certificates for free. It also attaches the KMS keys to the ALB to encrypt data in transit, which is not possible as ALB requires certificates and not keys.

• Launching instances within a single AZ and using a cluster placement group provides the lowest network latency and highest bandwidth between instances. This maximizes performance for an in-memory database and high-throughput application.

• Communications between instances in the same AZ and placement group are free, minimizing data transfer charges. Inter-AZ and public IP traffic can incur charges.

• A cluster placement group enables the instances to be placed close together within the AZ, allowing the high network throughput required. Partition groups span AZs, reducing bandwidth.

• Auto Scaling across zones could launch instances in AZs that increase data transfer charges. It may reduce network throughput, impacting performance.

This option is the most operationally efficient because it uses AWS Transfer Family to create an FTP server that can store incoming files in Amazon S3 Standard12, which is a low-cost and highly available storage service. It also uses AWS Lambda to process the files and delete them after they are processed, which is a serverless and scalable solution that does not require any batch scheduling or infrastructure management. It also uses S3 event notifications to invoke the Lambda function when the files arrive, which enables near real-time processing of the incoming data files3. Option A is less efficient because it uses Amazon S3 Glacier Flexible Retrieval, which is a cold storage class that has higher retrieval costs and longer retrieval times than Amazon S3 Standard. It also uses EventBridge rules to invoke the job nightly, which does not meet the requirement of processing incoming data files as soon as possible. Option B is less efficient because it uses an EBS volume to store incoming files, which is a block storage service that has higher costs and lower durability than Amazon S3. It also uses EventBridge rules to invoke the job nightly, which does not meet the requirement of processing incoming data files as soon as possible. Option C is less efficient because it uses an EBS volume to store incoming files, which is a block storage service that has higher costs and lower durability than Amazon S3. It also uses AWS Batch to process the files, which requires managing compute resources and job queues.

https://docs.aws.amazon.com/whitepapers/latest/disaster-recovery-workloads-on-aws/disaster-recovery-options-in-the-cloud.html

To resolve the issue of slow page loads for a rapidly growing e-commerce website hosted on AWS, a solutions architect can take the following two actions:

1. Set up an Amazon CloudFront distribution

2. Create a read replica for the RDS DB instance

Configuring an Amazon Redshift cluster is not relevant to this issue since Redshift is a data warehousing service and is typically used for the analytical processing of large amounts of data.

Hosting the dynamic web content in Amazon S3 may not necessarily improve performance since S3 is an object storage service, not a web application server. While S3 can be used to host static web content, it may not be suitable for hosting dynamic web content since S3 doesn't support server-side scripting or processing.

Configuring a Multi-AZ deployment for the RDS DB instance will improve high availability but may not necessarily improve performance.

To encrypt data at rest in Amazon RDS, you can use the encryption feature of Amazon RDS, which uses AWS Key Management Service (AWS KMS). With this feature, Amazon RDS encrypts each database instance with a unique key. This key is stored securely by AWS KMS. You can manage your own keys or use the default AWS-managed keys. When you enable encryption for a DB instance, Amazon RDS encrypts the underlying storage, including the automated backups, read replicas, and snapshots.

https://aws.amazon.com/getting-started/hands-on/filter-messages-published-to-topics/

This approach will allow the EC2 instance to access the internet and download the monthly security updates while still being located in a private subnet. By creating a NAT gateway and placing it in a public subnet, it will allow the instances in the private subnet to access the internet through the NAT gateway. And then, configure the private subnet route table to use the NAT gateway as the default route. This will ensure that all outbound traffic is directed through the NAT gateway, allowing the EC2 instance to access the internet while still maintaining the security of the private subnet.

The allowed block size is between a /28 netmask and /16 netmask. The CIDR block must not overlap with any existing CIDR block that's associated with the VPC. https://docs.aws.amazon.com/vpc/latest/userguide/configure-your-vpc.html

The combination of steps that will accomplish the task of making the web server accessible from everywhere on port 443 is to create a security group with a rule to allow TCP port 443 from source 0.0.0.0/0 (A) and to update the network ACL to allow inbound TCP port 443 from source 0.0.0.0/0 (C). This will ensure that traffic to port 443 is allowed both at the security group level and at the network ACL level, which will make the web server accessible from everywhere on port 443.

• Amazon S3 for large-scale, durable, and inexpensive storage of the video content. S3 storage costs are significantly lower than EFS. • Amazon EBS only temporarily during processing. By mounting an EBS volume only when a video needs to be processed, and unmounting it after, the time the content spends on the higher-cost EBS storage is minimized. • The EBS volume can be sized to match the workload needs for active processing, keeping costs lower. The volume does not need to store the entire video library long-term.

https://aws.amazon.com/blogs/security/how-to-use-aws-secrets-manager-rotate-credentials-amazon-rds-database-types-oracl e/

As they would like to retrieve the data with sub-millisecond, DynamoDB with DAX is the answer. DynamoDB supports some of the world's largest scale applications by providing consistent, single-digit millisecond response times at any scale. You can build applications with virtually unlimited throughput and storage.

https://aws.amazon.com/dynamodb/dax/?nc1=h_ls

The S3 Block Public Access feature allows you to restrict public access to S3 buckets and objects within the account. You can enable this feature at the account level to prevent any S3 bucket from being made public, regardless of the bucket policy settings. AWS Organizations can be used to apply a Service Control Policy (SCP) to the account to prevent IAM users from changing this setting, ensuring that all S3 objects remain private. This is a straightforward and effective solution that requires minimal operational overhead.

Amazon Transcribe now supports speaker labeling for streaming transcription. Amazon Transcribe is an automatic speech recognition (ASR) service that makes it easy for you to convert speech-to-text. In live audio transcription, each stream of audio may contain multiple speakers. Now you can conveniently turn on the ability to label speakers, thus helping to identify who is saying what in the output transcript. https://aws.amazon.com/about-aws/ whats-new/2020/08/amazon-transcribe-supports-speaker-labeling-streaming-transcription/

https://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/PrivateContent.html

This option will configure the development environment in the most cost-effective way as it reduces the number of instances running in the development environment and therefore reduces the cost of running the application. The development environment typically requires less resources than the production environment, and it is unlikely that the development environment will have periods of high traffic that would require a large number of instances. By reducing the maximum number of instances in the development environment's Auto Scaling group, the company can save on costs while still maintaining a functional development environment.

1) SQS FIFO queues guarantee that messages are received in the exact order they are sent. Using the payment ID as the message group ensures all messages for a payment ID are received sequentially. 2) Kinesis data streams can also enforce ordering on a per partition key basis. Using the payment ID as the partition key will ensure strict ordering of messages for each payment ID.

"The company wants to ensure that end users retain immediate access to all file types from the on-premises systems " - Cached volumes: low latency access to most recent data - Stored volumes: entire dataset is on premise, scheduled backups to S3 Hence Volume Gateway stored volume is the apt choice.

Move the data objects to S3 Standard-Infrequent Access (S3 Standard-IA) after 30 days - will meet the requirements of keeping the data immediately accessible with high availability and resiliency, while minimizing storage costs. S3 Standard-IA is designed for infrequently accessed data, and it provides a lower storage cost than S3 Standard, while still offering the same low latency, high throughput, and high durability as S3 Standard.

Auto scaling storage RDS will ease storage issues and migrating Oracle Pl/Sql to Aurora is cumbersome. Also Aurora has auto storage scaling by default. https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/USER_PIOPS.StorageTypes.html#USER_PIOPS.Aut oscaling

This option will scale up capacity faster in the morning to improve performance, but will still allow capacity to scale down during off hours. It achieves this as follows: • A target tracking action scales based on a CPU utilization target. By triggering at a lower CPU threshold in the morning, the Auto Scaling group will start scaling up sooner as traffic ramps up, launching instances before utilization gets too high and impacts performance. • Decreasing the cooldown period allows Auto Scaling to scale more aggressively, launching more instances faster until the target is reached. This speeds up the ramp-up of capacity. • However, unlike a scheduled action to set a fixed minimum/maximum capacity, with target tracking the group can still scale down during off hours based on demand. This helps minimize costs.

AWS Glue jobs are designed for extract, transform, and load (ETL) operations, which are perfect for transforming clinical trial data. AWS Glue integrates with AWS Key Management Service (KMS), allowing for customer-specific encryption keys, fulfilling the encryption requirement with minimal operational effort. Client-side encryption with AWS KMS ensures that the data is encrypted before it is sent to S3, aligning with the security needs specified in the scenario.

Key aspects:

AWS Glue: This managed ETL service simplifies data transformation, reduces operational overhead, and integrates seamlessly with KMS.

CSE-KMS: Client-side encryption with KMS ensures that the data is encrypted with customer-specific keys before it is processed or stored in S3, offering robust security??.

Minimal Operational Overhead: Compared to managing an EMR cluster, AWS Glue automates much of the process, making it a lower-effort solution.

AWS Documentation: According to the AWS Well-Architected Framework, encryption with AWS KMS offers strong security controls that meet the needs of industries requiring high levels of confidentiality??.

Configure an EC2 Auto Scaling scheduled scaling policy based on the monthly schedule is the best option because it allows for the proactive scaling of the EC2 instances before the monthly batch run begins. This will ensure that the application is able to handle the increased workload without experiencing downtime. The scheduled scaling policy can be configured to increase the number of instances in the Auto Scaling group a few hours before the batch run and then decrease the number of instances after the batch run is complete. This will ensure that the resources are available when needed and not wasted when not needed. The most appropriate solution to handle the increased workload during the monthly batch run and avoid downtime would be to configure an EC2 Auto Scaling scheduled scaling policy based on the monthly schedule.

https://docs.aws.amazon.com/autoscaling/ec2/userguide/ec2-auto-scaling-scheduled-scaling.html

The data retention period of a Kinesis data stream is the time period from when a record is added to when it is no longer accessible1. The default retention period for a Kinesis data stream is 24 hours, which can be extended up to 8760 hours (365 days)1. The data retention period can be updated by using the AWS Management Console, the AWS CLI, or the Kinesis Data Streams API1.

To meet the requirements of the scenario, the solutions architect should update the Kinesis Data Streams default settings by modifying the data retention period. The solutions architect should increase the retention period to a value that is greater than or equal to the frequency of consuming the data and writing it to S32. This way, the company can ensure that S3 receives all the data that the application sends to Kinesis Data Streams.

AWS Outposts is a fully managed service that delivers AWS infrastructure and services to virtually any on-premises or edge location for a consistent hybrid experience. AWS Outposts supports Amazon EKS, which is a managed service that makes it easy to run Kubernetes on AWS and on-premises. By installing an AWS Outposts rack in the company’s data center, the company can run containers in a Kubernetes environment using Amazon EKS and other AWS managed services, while keeping the data locally in the company’s data center and meeting the compliance requirements. AWS Outposts also provides a seamless connection to the local AWS Region for access to a broad range of AWS services.

Option A is not a valid solution because AWS Local Zones are not deployed in the company’s data center, but in large metropolitan areas closer to end users. AWS Local Zones are owned, managed, and operated by AWS, and they provide low-latency access to the public internet and the local AWS Region. Option B is not a valid solution because AWS Snowmobile is a service that transports exabytes of data to AWS using a 45-foot long ruggedized shipping container pulled by a semi-trailer truck. AWS Snowmobile is not designed for running containers or AWS managed services on-premises, but for large-scale data migration. Option D is not a valid solution because AWS Snowball Edge Storage Optimized is a device that provides 80 TB of HDD or 210 TB of NVMe storage capacity for data transfer and edge computing. AWS Snowball Edge Storage Optimized does not support Amazon EKS or other AWS managed services, and it is not suitable for running containers in a Kubernetes environment.

References:

AWS Outposts - Amazon Web Services

Amazon EKS on AWS Outposts - Amazon EKS

AWS Local Zones - Amazon Web Services

AWS Snowmobile - Amazon Web Services

[AWS Snowball Edge Storage Optimized - Amazon Web Services]

This solution meets the requirement of reducing the number of database reads while ensuring high availability for a batch application that consists of a backend with multiple Amazon RDS databases. Amazon RDS read replicas are copies of the primary database instance that can serve read-only traffic. You can create one or more read replicas for a primary database instance and connect to them using a special endpoint. Read replicas can improve the performance and availability of your application by offloading read queries from the primary database instance.

Option B is incorrect because using Amazon ElastiCache for Redis can provide a fast, in-memory data store that can cache frequently accessed data, but it does not support replication from Amazon RDS databases. Option C is incorrect because using Amazon Route 53 DNS caching can improve the performance and availability of DNS queries, but it does not reduce the number of database reads. Option D is incorrect because using Amazon ElastiCache for Memcached can provide a fast, in-memory data store that can cache frequently accessed data, but it does not support replication from Amazon RDS databases.

References:

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

By configuring scheduled scaling, the solutions architect can set the Auto Scaling group to automatically scale up to the desired compute level at a specific time (IAM) when the batch job starts and then automatically scale down after the job is complete. This will allow the desired EC2 capacity to be reached quickly and also help in reducing the cost.

Deploy Amazon API Gateway as an HTTPS endpoint and AWS Lambda to process and save the messages to an Amazon DynamoDB table. This option provides a highly available and scalable solution that can easily handle large amounts of data. It also integrates with other AWS services, making it easier to analyze and visualize the data for the security team.

Key phrase in the Question is must scale read and write capacity. Aurora is only for Read. Amazon DynamoDB has two read/write capacity modes for processing reads and writes on your tables: On-demand Provisioned (default, free-tier eligible) https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/HowItWorks.ReadWriteCapacityMode.html

Lambda server-less is scalable and elastic than EC2 api gateway solution

it allows the company to monitor costs and notify responsible stakeholders in the event of unusual spending. By creating an AWS Cost Anomaly Detection monitor in the AWS Billing and Cost Management console, the company can use a machine learning service that automatically detects and alerts on anomalous spend. By configuring alert thresholds, notification preferences, and root cause analysis, the company can prevent unusual spending and identify its source. References:

AWS Cost Anomaly Detection

Creating a Cost Anomaly Monitor

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.

https://aws.amazon.com/vpc/faqs/#:~:text=Can%20a%20subnet% 20span%20Availability,within%20a%20single%20Availability%20Zone.

the best solution is to implement Amazon ElastiCache to cache the large datasets, which will store the frequently accessed data in memory, allowing for faster retrieval times. This can help to alleviate the frequent calls to the database, reduce latency, and improve the overall performance of the backend tier.

The best option is to use AWS CloudTrail to find the desired information. AWS CloudTrail is a service that enables governance, compliance, operational auditing, and risk auditing of AWS account activities. CloudTrail can be used to log all changes made to resources in an AWS account, including changes made by IAM users, EC2 instances, AWS management console, and other AWS services. By using CloudTrail, the solutions architect can identify the IAM user who made the configuration changes to the security group rules.

UESTION NO: 549

A company has a three-tier environment on AWS that ingests sensor data from its users' devices The traffic flows through a Network Load Balancer (NIB) then to Amazon EC2 instances for the web tier and finally to EC2 instances for the application tier that makes database calls

What should a solutions architect do to improve the security of data in transit to the web tier?

A. Configure a TLS listener and add the server certificate on the NLB

B. Configure AWS Shield Advanced and enable AWS WAF on the NLB

C. Change the load balancer to an Application Load Balancer and attach AWS WAF to it

D. Encrypt the Amazon Elastic Block Store (Amazon EBS) volume on the EC2 instances using AWS Key Management Service (AWS KMS)

Answer: A

A: How do you protect your data in transit?

Best Practices:

Implement secure key and certificate management: Store encryption keys and certificates securely and rotate them at appropriate time intervals while applying strict access control; for example, by using a certificate management service, such as AWS Certificate Manager (ACM).

Enforce encryption in transit: Enforce your defined encryption requirements based on appropriate standards and recommendations to help you meet your organizational, legal, and compliance requirements.

Automate detection of unintended data access: Use tools such as GuardDuty to automatically detect attempts to move data outside of defined boundaries based on data classification level, for example, to detect a trojan that is copying data to an unknown or untrusted network using the DNS protocol.

Authenticate network communications: Verify the identity of communications by using protocols that support authentication, such as Transport Layer Security (TLS) or IPsec.

https://wa.aws.amazon.com/wat.question.SEC_9.en.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

"You cannot reference the security group of a peer VPC that's in a different Region. Instead, use the CIDR block of the peer VPC." https://docs.aws.amazon.com/vpc/latest/peering/vpc-peering-security-groups.html

If you have resources in multiple Availability Zones and they share one NAT gateway, and if the NAT gateway’s Availability Zone is down, resources in the other Availability Zones lose internet access. To create an Availability Zone-independent architecture, create a NAT gateway in each Availability Zone and configure your routing to ensure that resources use the NAT gateway in the same Availability Zone. https://docs.aws.amazon.com/vpc/latest/userguide/vpc-nat-gateway.html#nat-gateway-basics

https://docs.aws.amazon.com/waf/latest/developerguide/web-acl.html

A rate-based rule in AWS WAF allows the security team to configure thresholds that trigger rate-based rules, which enable AWS WAF to track the rate of requests for a specified time period and then block them automatically when the threshold is exceeded. This provides the ability to prevent HTTP flood attacks with minimal operational overhead.

A NAT gateway is a type of network address translation (NAT) device that enables instances in a private subnet to connect to the internet or other AWS services, but prevents the internet from initiating connections with those instances1. A NAT gateway is a managed service that requires minimal operational maintenance and can handle up to 45 Gbps of bursty traffic1. A NAT gateway is suitable for scenarios where EC2 instances in private subnets need to communicate with a license server over the internet, such as the three-tier web application in the scenario1.

To meet the requirements of the scenario, the solutions architect should provision a NAT gateway in a public subnet. The solutions architect should also modify each private subnet’s route table with a default route that points to the NAT gateway1. This way, the EC2 instances that run in private subnets can access the license server over the internet through the NAT gateway.

Amazon S3 File Gateway is a service that provides a file-based interface to Amazon S3, which appears as a network file share. It enables you to store and retrieve Amazon S3 objects through standard file storage protocols such as SMB. S3 File Gateway can also cache frequently accessed data locally for low-latency access. S3 Lifecycle policy is a feature that allows you to define rules that automate the management of your objects throughout their lifecycle. You can use S3 Lifecycle policy to transition objects to different storage classes based on their age and access patterns. S3 Glacier Deep Archive is a storage class that offers the lowest cost for long-term data archiving, with a retrieval time of 12 hours or 48 hours. This solution will meet the requirements, as it allows the company to store large files in S3 with SMB file access, and to move the files to S3 Glacier Deep Archive after 7 days for cost savings and compliance.

References:

1 provides an overview of Amazon S3 File Gateway and its benefits.

2 explains how to use S3 Lifecycle policy to manage object storage lifecycle.

3 describes the features and use cases of S3 Glacier Deep Archive storage class.

Creating a read replica of the primary RDS database will offload the read-only SQL queries from the primary database, which will help to improve the performance of the web application. Read replicas are exact copies of the primary database that can be used to handle read-only traffic, which will reduce the load on the primary database and improve the performance of the web application. This solution can be implemented with minimal changes to the existing web application, as the business analysts can continue to run their queries on the read replica without modifying the code.

An Auto Scaling group is a collection of EC2 instances that share similar characteristics and can be scaled in or out automatically based on demand. An Auto Scaling group can be placed behind an Application Load Balancer, which is a type of Elastic Load Balancing load balancer that distributes incoming traffic across multiple targets in multiple Availability Zones. This solution will improve the resiliency of the web tier by providing high availability, scalability, and fault tolerance. An Amazon RDS Multi-AZ deployment is a configuration that automatically creates a primary database instance and synchronously replicates the data to a standby instance in a different Availability Zone. When a failure occurs, Amazon RDS automatically fails over to the standby instance without manual intervention. This solution will improve the resiliency of the database tier by providing data redundancy, backup support, and availability. This combination of steps will meet the requirements with minimal changes to the application during the migration.

References:

1 describes the concept and benefits of an Auto Scaling group.

2 provides an overview of Application Load Balancers and their benefits.

3 explains how Amazon RDS Multi-AZ deployments work and their benefits.

https://docs.aws.amazon.com/tag-editor/latest/userguide/tagging.html

https://aws.amazon.com/solutions/implementations/instance-scheduler-on-aws/?nc1=h_ls

The Instance Scheduler on AWS solution automates the starting and stopping of Amazon Elastic Compute Cloud (Amazon EC2) and Amazon Relational Database Service (Amazon RDS) instances. This solution helps reduce operational costs by stopping resources that are not in use and starting them when they are needed1. The solution allows you to define custom schedules and periods using a command line interface (CLI) or an SSM maintenance window1. You can also choose between different payment options for the reserved DB instances, such as No Upfront, Partial Up front, or All Upfront2.

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/IAM/latest/UserGuide/id_roles_common-scenarios_third-party.html

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

This answer is correct because it provides a resilient and durable replacement for the on-premises file share that is compatible with Windows IIS web servers. Amazon FSx for Windows File Server is a fully managed service that provides shared file storage built on Windows Server. It supports the SMB protocol and integrates with Microsoft Active Directory, which enables seamless access and authentication for Windows-based applications. Amazon FSx for Windows File Server also offers the following benefits:

Resilience: Amazon FSx for Windows File Server can be deployed in multiple Availability Zones, which provides high availability and failover protection. It also supports automatic backups and restores, as well as self-healing features that detect and correct issues.

Durability: Amazon FSx for Windows File Server replicates data within and across Availability Zones, and stores data on highly durable storage devices. It also supports encryption at rest and in transit, as well as file access auditing and data deduplication.

Performance: Amazon FSx for Windows File Server delivers consistent sub-millisecond latencies and high throughput for file operations. It also supports SSD storage, native Windows features such as Distributed File System (DFS) Namespaces and Replication, and user-driven performance scaling.

References:

Amazon FSx for Windows File Server

Using Microsoft Windows file shares

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.

AWS DataSync is a data transfer service that makes it easy for you to move large amounts of data online between on-premises storage and AWS storage services over the internet or AWS Direct Connect. DataSync automatically encrypts your data in transit using TLS encryption, and verifies data integrity during transfer using checksums. DataSync can transfer data up to 10 times faster than open-source tools, and reduces operational overhead by simplifying and automating tasks such as scheduling, monitoring, and resuming transfers. References: https://aws.amazon.com/datasync/

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

Amazon API Gateway is a fully managed service that makes it easy for developers to create, publish, maintain, monitor, and secure APIs at any scale. AWS Lambda is a serverless compute service that lets you run code without provisioning or managing servers. Lambda scales automatically based on the incoming requests, but it may take some time to initialize new instances of your function if there is a sudden increase in demand. This may result in high latency or cold starts for your API. To avoid this, you can use provisioned concurrency, which ensures that your function is initialized and ready to respond at any time. Provisioned concurrency also helps you achieve consistent low latency for your API by reducing the impact of scaling on performance. References: https://docs.aws.amazon.com/apigateway/latest/developerguide/http-api-develop-integrations-lambda.html https://docs.aws.amazon.com/lambda/latest/dg/co nfiguration-concurrency.html

https://docs.aws .amazon.com/awscloudtrail/latest/userguide/best-practices-security.html#:~:text=The%20CloudTrail%20trail,time%20has%20passed.

it allows the company to implement a disaster recovery (DR) solution for the application that has a recovery time objective (RTO) of less than 4 hours and uses the fewest possible AWS resources during normal operations. By creating Amazon Machine Images (AMIs) to back up the EC2 instances and copying the AMIs to a secondary AWS Region, the company can create point-in-time snapshots of the application and store them in a different geographical location. By automating infrastructure deployment in the secondary Region by using AWS CloudFormation, the company can quickly launch a stack of resources from a template in case of a disaster. This is a cost-effective and operationally efficient way to implement a DR solution for EC2 instances. References:

Amazon Machine Images (AMI)

Copying an AMI

AWS CloudFormation

Working with Stacks

https://docs.aws.amazon.com/IAM/latest/UserGuide/access_policies.html

https://docs.aws.amazon.com/accounts/latest/reference/best-practices-root-user.html * Enable AWS multi-factor authentication (MFA) on your AWS account root user. For more information, see Using multi-factor authentication (MFA) in AWS in the IAM User Guide. * Never share your AWS account root user password or access keys with anyone. * Use a strong password to help protect access to the AWS Management Console. For information about managing your AWS account root user password, see Changing the password for the root user.

S3 Storage Lens is a fully managed S3 storage analytics solution that provides a comprehensive view of object storage usage, activity trends, and recommendations to optimize costs. Storage Lens allows you to analyze object access patterns across all of your S3 buckets and generate detailed metrics and reports.

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.

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

To reduce startup latency for Lambda functions that run on Java 11, Lambda SnapStart is a suitable solution. Lambda SnapStart is a feature that enables faster cold starts and lower outlier latencies for Java 11 functions. Lambda SnapStart uses a pre-initialized Java Virtual Machine (JVM) to run the functions, which reduces the initialization time and memory footprint. Lambda SnapStart does not incur any additional charges.

References:

Lambda SnapStart for Java 11 Functions

Lambda SnapStart FAQs

Kubernetes API requests within your cluster's VPC (such as node to control plane communication) use the private VPC endpoint. https://docs.aws.amazon.com/eks/latest/userguide/cluster-endpoint.html

https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resour ce-ecs-taskdefinition.html

AWS CloudFormation is a service that helps you model and set up your AWS resources by using templates that describe all the resources that you want, such as Auto Scaling groups, load balancers, and databases. You can use AWS CloudFormation to deploy your infrastructure in an automated and consistent way across multiple environments and regions. You can also use AWS CloudFormation to update or delete your infrastructure as a single unit.

Reference URLs:

1 https://aws.amazon.com/cloudformation/

2 https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/Welcome.html

3 https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/cfn-whatis-concepts.html

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

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

AWS Lambda is a service that lets users run code without provisioning or managing servers. It automatically scales and manages the underlying compute resources for the code. It supports multiple languages, such as Java, Python, Node.js, and Go1. By using AWS Lambda for the REST API, the solution can meet the requirements of 1 GB of memory and minimal administrative effort.

Amazon RDS is a service that makes it easy to set up, operate, and scale a relational database in the cloud. It provides cost-efficient and resizable capacity while automating time-consuming administration tasks such as hardware provisioning, database setup, patching and backups. It supports multiple database engines, such as MySQL, PostgreSQL, Oracle, and SQL Server2. By using Amazon RDS for the data store, the solution can meet the requirements of 2 GB of storage and a relational format.

A. Amazon EC2. This solution will not meet the requirement of minimal administrative effort, as Amazon EC2 is a service that provides virtual servers in the cloud that users have to configure and manage themselves. It requires users to choose an instance type, an operating system, a security group, and other options3.

D. Amazon DynamoDB. This solution will not meet the requirement of a relational format, as Amazon DynamoDB is a service that provides a key-value and document database that delivers single-digit millisecond performance at any scale. It is a non-relational or NoSQL database that does not support joins or transactions.

E. Amazon Elastic Kubernetes Services (Amazon EKS). This solution will not meet the requirement of minimal administrative effort, as Amazon EKS is a service that provides a fully managed Kubernetes service that users have to configure and manage themselves. It requires users to create clusters, nodes groups, pods, services, and other Kubernetes resources.

Reference URL: https://aws.amazon.com/lambda/

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

This approach allows users to upload files directly to S3 without passing through the application servers, reducing the load on the application and improving scalability. It leverages the client-side capabilities to handle the file uploads and offloads the processing to S3.

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

This answer is correct because it meets the requirements of accessing a shared file system that is highly durable, can recover data to another AWS Region, and can provide a mount target in each Availability Zone within a Region. Amazon FSx for NetApp ONTAP is a fully managed service that provides enterprise-grade data management and storage for your Windows and Linux applications. You can use Amazon FSx for NetApp ONTAP to create file systems that span multiple Availability Zones within an AWS Region, providing high availability and durability. You can also use AWS Backup to manage the replication of your file systems to another AWS Region, with a recovery point objective (RPO) of 8 hours or less. AWS Backup is a fully managed backup service that automates and centralizes backup of data over AWS services. You can use AWS Backup to create backup policies and monitor activity for your AWS resources in one place.

References:

https://docs.aws.amazon.com/fsx/latest/ONTAPGuide/what-is.html

https://docs.aws.amazon.com/aws-backup/latest/devguide/whatisbackup.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

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

{

"Version": "2012-10-17",

"Statement": [

{

"Action": [

"s3:ListBucket",

"s3:DeleteObject"

],

"Resource": [

"arn:aws:s3:::"

],

"Effect": "Allow",

},

{

"Action": "s3:*DeleteObject",

"Resource": [

"arn:aws:s3:::/*" # <- The policy clause kludge "added" to match the solution (Q248.1) example

],

"Effect": "Allow"

}

]

}

"An active, long-running transaction can slow the process of creating the read replica. We recommend that you wait for long-running transactions to complete before creating a read replica. If you create multiple read replicas in parallel from the same source DB instance, Amazon RDS takes only one snapshot at the start of the first create action. When creating a read replica, there are a few things to consider. First, you must enable automatic backups on the source DB instance by setting the backup retention period to a value other than 0. This requirement also applies to a read replica that is the source DB instance for another read replica" https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/USER_ReadRepl.html

To store and retrieve reports that are frequently accessed during the first week and must be stored for several years, S3 Standard and S3 Glacier are suitable solutions. S3 Standard offers high durability, availability, and performance for frequently accessed data. S3 Glacier offers secure and durable storage for long-term data archiving at a low cost. S3 Lifecycle rules can be used to transition the reports from S3 Standard to S3 Glacier after 7 days, which can reduce storage costs. S3 Glacier also supports retrieval within 6 hours.

References:

Storage Classes

Object Lifecycle Management

Retrieving Archived Objects from Amazon S3 Glacier

An edge-optimized API Gateway is a way to create RESTful APIs that can access multiple DynamoDB tables through AWS Lambda functions. The edge-optimized API Gateway provides low latency and high performance by caching API responses at CloudFront edge locations. The AWS Lambda functions can use the AWS SDK to query or scan the DynamoDB tables and return the data to the API Gateway. This solution meets all the requirements of the question, while the other options do not. References:

https://aws.amazon.com/blogs/compute/understanding-database-options-for-your-serverless-web-applications/

https://aws.amazon.com/getting-started/hands-on/build-serverless-web-app-lambda-apigateway-s3-dynamodb-cognito/module-3/

https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/best-practices.html

Service Control Policies (SCP): SCPs are an integral part of AWS Organizations and allow you to set fine-grained permissions on the organizational units (OUs) within your AWS Organization. SCPs provide central control over the maximum permissions that can be granted to member accounts, including the root user. Denying Access to Billing Information: By creating an SCP and attaching it to the root OU, you can explicitly deny access to billing information for all accounts within the organization. SCPs can be used to restrict access to various AWS services and actions, including billing-related services. Granular Control: SCPs enable you to define specific permissions and restrictions at the organizational unit level. By denying access to billing information at the root OU, you can ensure that no member accounts, including root users, have access to the billing information.

This option is the best solution because it allows the company to migrate the container application to AWS with minimal changes and leverage a managed service to run the Kubernetes cluster and the message queue. By using Amazon EKS, the company can run the container application on a fully managed Kubernetes control plane that is compatible with the existing Kubernetes tools and plugins. Amazon EKS handles the provisioning, scaling, patching, and security of the Kubernetes cluster, reducing the operational overhead and complexity. By using Amazon MQ, the company can use a fully managed message broker service that supports AMQP and other popular messaging protocols. Amazon MQ handles the administration, maintenance, and scaling of the message broker, ensuring high availability, durability, and security of the messages.

A. Migrate the container application to Amazon Elastic Container Service (Amazon ECS) Use Amazon Simple Queue Service (Amazon SQS) to retrieve the messages. This option is not optimal because it requires the company to change the container orchestration platform from Kubernetes to ECS, which can introduce additional complexity and risk. Moreover, it requires the company to change the messaging protocol from AMQP to SQS, which can also affect the application logic and performance. Amazon ECS and Amazon SQS are both fully managed services that simplify the deployment and management of containers and messages, but they may not be compatible with the existing application architecture and requirements.

C. Use highly available Amazon EC2 instances to run the application Use Amazon MQ to retrieve the messages. This option is not ideal because it requires the company to manage the EC2 instances that host the container 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, the company would need to install and maintain the Kubernetes software on the EC2 instances, which can also add complexity and risk. Amazon MQ is a fully managed message broker service that supports AMQP and other popular messaging protocols, but it cannot compensate for the lack of a managed Kubernetes service.

D. Use AWS Lambda functions to run the application Use Amazon Simple Queue Service (Amazon SQS) to retrieve the messages. This option is not feasible because AWS Lambda does not support running container applications directly. Lambda functions are executed in a sandboxed environment that is isolated from other functions and resources. To run container applications on Lambda, the company would need to use a custom runtime or a wrapper library that emulates the container API, which can introduce additional complexity and overhead. Moreover, Lambda functions have limitations in terms of available CPU, memory, and runtime, which may not suit the application needs. Amazon SQS is a fully managed message queue service that supports asynchronous communication, but it does not support AMQP or other messaging protocols.

References:

1 Amazon Elastic Kubernetes Service - Amazon Web Services

2 Amazon MQ - Amazon Web Services

3 Amazon Elastic Container Service - Amazon Web Services

4 AWS Lambda FAQs - Amazon Web Services

Deletion protection is a feature of DynamoDB that prevents accidental deletion of tables. When deletion protection is enabled, you cannot delete a table unless you explicitly disable it first. This adds an extra layer of security and reduces the risk of data loss and operational disruption. Deletion protection is easy to enable and disable using the AWS Management Console, the AWS CLI, or the DynamoDB API. This solution has the least operational overhead, as you do not need to create, manage, or invoke any additional resources or services. References:

Using deletion protection to protect your table

Preventing Accidental Table Deletion in DynamoDB

Amazon DynamoDB now supports table deletion protection

To use AWS Budgets to create and manage budgets for different AWS accounts, the company needs to do the following steps:

Use AWS Budgets to create a budget for each AWS account that needs a different budget amount. The budget can be based on cost or usage metrics, and can have different time periods, filters, and thresholds. The company can set the budget amount under the Billing dashboards of the required AWS accounts1.

Create an IAM role for AWS Budgets to run budget actions with the required permissions. A budget action is a response that AWS Budgets initiates when a budget exceeds a specified threshold. The IAM role allows AWS Budgets to perform actions on behalf of the company, such as applying an IAM policy or a service control policy (SCP) to restrict the provisioning of additional resources2.

Add an alert to notify the company when each account meets its budget threshold. The alert can be sent via email or Amazon SNS. The company can also add a budget action that selects the IAM role created and the appropriate SCP to prevent provisioning of additional resources. An SCP is a type of policy that can be applied to an AWS account or an organizational unit (OU) within AWS Organizations. An SCP can limit the actions that users and roles can perform in the account or OU3.

References:

4: https://aws.amazon.com/budgets/

1: https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/budgets-create.html

2: https://docs.aws.amazon.com/cost-management/latest/userguide/budgets-controls.html

3: https://docs.aws.amazon.com/organizations/latest/userguide/orgs_manage_policies_scps.html

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?

The most suitable solution for optimizing the user experience by providing the lowest latency for content uploads is to upload and store content in Amazon S3 and use S3 Transfer Acceleration for the uploads. This solution will enable the company to leverage the AWS global network and edge locations to speed up the data transfer between the users and the S3 buckets.

Amazon S3 is a storage service that provides scalable, durable, and highly available object storage for any type of data. Amazon S3 allows users to store and retrieve data from anywhere on the web, and offers various features such as encryption, versioning, lifecycle management, and replication1.

S3 Transfer Acceleration is a feature of Amazon S3 that helps users transfer data to and from S3 buckets more quickly. S3 Transfer Acceleration works by using optimized network paths and Amazon’s backbone network to accelerate data transfer speeds. Users can enable S3 Transfer Acceleration for their buckets and use a distinct URL to access them, such as .s3-accelerate.amazonaws.com2.

The other options are not correct because they either do not provide the lowest latency or are not suitable for the use case. Uploading and storing content in Amazon S3 and using Amazon CloudFront for the uploads is not correct because this solution is not designed for optimizing uploads, but rather for optimizing downloads. Amazon CloudFront is a content delivery network (CDN) that helps users distribute their content globally with low latency and high transfer speeds. CloudFront works by caching the content at edge locations around the world, so that users can access it quickly and easily from anywhere3. Uploading content to Amazon EC2 instances in the Region that is closest to the user and copying the data to Amazon S3 is not correct because this solution adds unnecessary complexity and cost to the process. Amazon EC2 is a computing service that provides scalable and secure virtual servers in the cloud. Users can launch, stop, or terminate EC2 instances as needed, and choose from various instance types, operating systems, and configurations4. Uploading and storing content in Amazon S3 in the Region that is closest to the user and using multiple distributions of Amazon CloudFront is not correct because this solution is not cost-effective or efficient for the use case. As mentioned above, Amazon CloudFront is a CDN that helps users distribute their content globally with low latency and high transfer speeds. However, creating multiple CloudFront distributions for each Region would incur additional charges and management overhead, and would not be necessary since 90% of the content is consumed within the same Region where it is uploaded3.

References:

What Is Amazon Simple Storage Service? - Amazon Simple Storage Service

Amazon S3 Transfer Acceleration - Amazon Simple Storage Service

What Is Amazon CloudFront? - Amazon CloudFront

What Is Amazon EC2? - Amazon Elastic Compute Cloud

Amazon EFS is a fully managed, elastic, and scalable file system that can be shared between multiple containers. It provides high availability and fault tolerance by replicating data across multiple Availability Zones. Amazon EFS is compatible with Amazon EKS and AWS Fargate, and can be registered in a StorageClass object on an EKS cluster. Amazon EBS volumes are not supported by AWS Fargate, and cannot be shared between multiple containers without using EBS Multi-Attach, which has limitations and performance implications. EBS Multi-Attach also requires the volumes to be in the same Availability Zone as the worker nodes, which reduces availability and fault tolerance. Synchronizing data between multiple EFS file systems using AWS Lambda is unnecessary, complex, and prone to errors. References:

Amazon EFS Storage Classes

Amazon EKS Storage Classes

Amazon EBS Multi-Attach

This solution will meet the requirements because Amazon FSx for NetApp ONTAP is a fully managed service that provides highly reliable, scalable, and feature-rich file storage built on NetApp’s popular ONTAP file system. FSx for ONTAP supports both NFS and SMB protocols, which means it can be accessed by both Linux and Windows applications without code changes. FSx for ONTAP also eliminates data duplication and inefficient resource usage by automatically tiering infrequently accessed data to a lower-cost storage tier and providing storage efficiency features such as deduplication and compression. FSx for ONTAP also integrates with other AWS services such as Amazon S3, AWS Backup, and AWS CloudFormation. By migrating the applications to Amazon EC2 instances, the company can leverage the scalability, security, and performance of AWS compute resources. 

This option is the most operationally efficient way to meet the requirements because it allows the company to monitor and analyze the database login activity across all the accounts in the organization. By publishing the Aurora general logs to a log group in Amazon CloudWatch Logs, the company can enable the logging of the database connections, disconnections, and failed authentication attempts. By exporting the log data to a central Amazon S3 bucket, the company can store the log data in a durable and cost-effective way and use other AWS services or tools to perform further analysis or alerting on the log data. For example, the company can use Amazon Athena to query the log data in Amazon S3, or use Amazon SNS to send notifications based on the log data.

A. Attach service control policies (SCPs) to the root of the organization to identify the failed login attempts. This option is not effective because SCPs are not designed to identify the failed login attempts, but to restrict the actions that the users and roles can perform in the member accounts of the organization. SCPs are applied to the AWS API calls, not to the database login attempts. Moreover, SCPs do not provide any logging or analysis capabilities for the database activity.

B. Enable the Amazon RDS Protection feature in Amazon GuardDuty for the member accounts of the organization. This option is not optimal because the Amazon RDS Protection feature in Amazon GuardDuty is not available for Aurora PostgreSQL databases, but only for Amazon RDS for MySQL and Amazon RDS for MariaDB databases. Moreover, the Amazon RDS Protection feature does not monitor the database login attempts, but the network and API activity related to the RDS instances.

D. Publish all the Aurora PostgreSQL database events in AWS CloudTrail to a central Amazon S3 bucket. This option is not sufficient because AWS CloudTrail does not capture the database login attempts, but only the AWS API calls made by or on behalf of the Aurora PostgreSQL database. For example, AWS CloudTrail can record the events such as creating, modifying, or deleting the database instances, clusters, or snapshots, but not the events such as connecting, disconnecting, or failing to authenticate to the database.

References:

1 Working with Amazon Aurora PostgreSQL - Amazon Aurora

2 Working with log groups and log streams - Amazon CloudWatch Logs

3 Exporting Log Data to Amazon S3 - Amazon CloudWatch Logs

[4] Amazon GuardDuty FAQs

[5] Logging Amazon RDS API Calls with AWS CloudTrail - Amazon Relational Database Service

This solution provides the appropriate user access most cost-effectively because it uses the Amazon S3 Intelligent-Tiering storage class, which automatically optimizes storage costs by moving data to the most cost-effective access tier when access patterns change, without performance impact or operational overhead1. This storage class is ideal for data with unknown, changing, or unpredictable access patterns, such as photos that are heavily viewed for months or less than a week. By storing the photo metadata and its S3 location in DynamoDB, the application can quickly query and retrieve the relevant photos for each user. DynamoDB is a fast, scalable, and fully managed NoSQL database service that supports key-value and document data models2.

References: 1: Amazon S3 Intelligent-Tiering Storage Class | AWS3, Overview section2: Amazon DynamoDB - NoSQL Cloud Database Service4, Overview section.

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

A public NAT gateway enables instances in a private subnet to send outbound traffic to the internet, while preventing the internet from initiating connections with the instances. A public NAT gateway requires an elastic IP address and a route to the internet gateway for the VPC. A private NAT gateway enables instances in a private subnet to connect to other VPCs or on-premises networks through a transit gateway or a virtual private gateway. A private NAT gateway does not require an elastic IP address or an internet gateway. Both private and public NAT gateways map the source private IPv4 address of the instances to the private IPv4 address of the NAT gateway, but in the case of a public NAT gateway, the internet gateway then maps the private IPv4 address of the public NAT gateway to the elastic IP address associated with the NAT gateway. When sending response traffic to the instances, whether it’s a public or private NAT gateway, the NAT gateway translates the address back to the original source IP address.

Creating public NAT gateways in the same private subnets as the EC2 instances (option A) is not a valid solution, as the NAT gateways would not have a route to the internet gateway. Creating private NAT gateways in the same private subnets as the EC2 instances (option B) is also not a valid solution, as the instances would not be able to access the internet through the private NAT gateways. Creating private NAT gateways in public subnets in the same VPCs as the EC2 instances (option D) is not a valid solution either, as the internet gateway would drop the traffic from the private NAT gateways.

Therefore, the only valid solution is to create public NAT gateways in public subnets in the same VPCs as the EC2 instances (option C), as this would allow the instances to access the internet through the public NAT gateways and the internet gateway. References:

NAT gateways - Amazon Virtual Private Cloud

NAT gateway use cases - Amazon Virtual Private Cloud

Amazon Web Services – Introduction to NAT Gateways

What is AWS NAT Gateway? - KnowledgeHut

This option is the most cost-effective solution because it leverages the Spot Instances, which are unused EC2 instances that are available at up to 90% discount compared to On-Demand prices. Spot Instances can be interrupted by AWS when the demand for On-Demand instances increases, but since the batch jobs are fault-tolerant and can be reprocessed by another instance, this is not a major issue. By using a launch template, the company can specify the configuration of the Spot Instances, such as the instance type, the operating system, and the user data. By using an Auto Scaling group, the company can automatically scale the number of Spot Instances based on the CPU usage, which reflects the load of the batch jobs. This way, the company can optimize the performance and the cost of the EC2 instances for the nightly batch jobs.

A. Purchase a 1-year Savings Plan for Amazon EC2 that covers the instance family of the Auto Scaling group that the batch job uses. This option is not optimal because it requires a commitment to a consistent amount of compute usage per hour for a one-year term, regardless of the instance type, size, region, or operating system. This can limit the flexibility and scalability of the Auto Scaling group and result in overpaying for unused compute capacity. Moreover, Savings Plans do not provide a capacity reservation, which means the company still needs to reserve capacity with On-Demand Capacity Reservations and pay lower prices with Savings Plans.

B. Purchase a 1-year Reserved Instance for the specific instance type and operating system of the instances in the Auto Scaling group that the batch job uses. This option is not ideal because it requires a commitment to a specific instance configuration for a one-year term, which can reduce the flexibility and scalability of the Auto Scaling group and result in overpaying for unused compute capacity. Moreover, Reserved Instances do not provide a capacity reservation, which means the company still needs to reserve capacity with On-Demand Capacity Reservations and pay lower prices with Reserved Instances.

D. Create a new launch template for the Auto Scaling group Increase the instance size Set a policy to scale out based on CPU usage. This option is not cost-effective because it does not take advantage of the lower prices of Spot Instances. Increasing the instance size can improve the performance of the batch jobs, but it can also increase the cost of the On-Demand instances. Moreover, scaling out based on CPU usage can result in launching more instances than needed, which can also increase the cost of the system.

References:

1 Spot Instances - Amazon Elastic Compute Cloud

2 Launch templates - Amazon Elastic Compute Cloud

3 Auto Scaling groups - Amazon EC2 Auto Scaling

[4] Savings Plans - Amazon EC2 Reserved Instances and Other AWS Reservation Models