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

AmazonS3 with the Standard storage classis the best solution:

Encryption: SSE-S3 ensures server-side encryption of the data, meeting compliance requirements.

Immediate access: The Standard storage class provides low-latency and high-throughput access to data.

Multi-location storage: Cross-Region replication ensures data is stored in multiple AWS Regions for redundancy.

Why Other Options Are Not Ideal:

Option B:

Amazon EFS is more costly and suited for file systems rather than object storage.Not cost-effective.

Option C:

Amazon EBS is block storage and not optimized for object storage like PDFs. Backup schedules do not ensure immediate availability.Not suitable.

Option D:

S3 Glacier Flexible Retrieval is designed for archival, not immediate access.Does not meet access requirements.

AWS References:

Amazon S3 Standard Storage:AWS Documentation - S3 Storage Classes

Amazon S3 Cross-Region Replication:AWS Documentation - Cross-Region Replication

AWS Encryption Options:AWS Documentation - S3 Encryption

Amazon DocumentDB global clusters support managed cross-region failover, allowing you to promote a secondary region to become the new primary with minimal downtime. This ensures business continuity during maintenance or regional disruptions.

Reference Extract:

"Amazon DocumentDB global clusters support managed cross-Region failover, allowing you to recover quickly from regional disruptions with minimal downtime."

Source: AWS Certified Solutions Architect – Official Study Guide, DocumentDB and Resiliency section.

AWS WAF (Web Application Firewall) is designed to protect public-facing web applications from common web exploits and allows for deep inspection of HTTP and HTTPS requests. By enabling logging on AWS WAF, you can gain insights into traffic flow, request sources, and blocked requests, which improves both security visibility and posture. Integrating AWS WAF logging with Amazon Kinesis Data Firehose allows automatic, near-real-time delivery of log data to Amazon S3 for analysis, reporting, or integration with analytics tools. This solution not only secures the website but also enables comprehensive traffic analysis, satisfying both requirements efficiently.

Reference Extract from AWS Documentation / Study Guide:

"AWS WAF provides detailed logs of web requests, which can be delivered to Amazon S3 via Amazon Kinesis Data Firehose. This logging enables analysis of web traffic and sources, supporting both security and operational monitoring."

Source: AWS Certified Solutions Architect – Official Study Guide, Security and Compliance section; AWS WAF Developer Guide (Logging and Monitoring).

Explanation (AWS Docs):

Although the question says “before sending it,” AWS best practice for sensitive data is SSE-KMS (Server-side encryption with AWS KMS keys), which gives full key usage auditing. It integrates with AWS KMS and provides compliance-friendly encryption at rest automatically.

“SSE-KMS uses AWS Key Management Service to manage encryption keys. SSE-KMS also provides an audit trail of key usage.”

— Protecting Data Using Server-Side Encryption

Why not D?

Client-side encryption requires custom key management and adds operational overhead. C is simpler and compliant.

EBS gp3 volumes allow you to independently configure IOPS and throughput, up to 16,000 IOPS, regardless of the volume size, and at a lower cost compared to io1/io2 provisioned IOPS volumes. This meets the requirement for cost-effective, predictable IOPS performance for Amazon RDS for PostgreSQL.

AWS Documentation Extract:

"With gp3 volumes, you can provision performance independent of storage capacity, up to 16,000 IOPS. This allows for cost-effective scaling for applications that require high performance at a lower price point compared to io1."

(Source: Amazon EBS documentation, gp3 Volumes)

A: gp2 ties IOPS to volume size; 5 TiB is wasteful if only 15,000 IOPS are needed.

B: io1 works but is significantly more expensive than gp3 for most workloads.

D: Magnetic volumes do not support high IOPS.

Amazon EBS io2 Block Express volumes are designed to deliver sub-millisecond latency and up to 256,000 IOPS per volume, with durability and high availability. This makes io2 Block Express the recommended choice for workloads requiring very high and predictable IOPS, such as enterprise databases and high-transaction-rate applications.

Reference Extract:

"EBS io2 Block Express volumes deliver up to 256,000 IOPS and sub-millisecond latency, supporting high-performance, high-durability workloads."

Source: AWS Certified Solutions Architect – Official Study Guide, EBS Performance section.

Option B: FSx for Lustre is designed for HPC workloads with high IOPS.

Option E: A cluster placement group ensures low-latency networking for HPC analytics workloads.

Option A: Amazon EFS is not optimized for HPC.

Option D: Mountpoint for S3 does not meet high IOPS needs.

To ensure ALB access only via CloudFront, AWS prescribes restricting the origin to traffic from CloudFront. For ALB origins, the standard pattern is to allow inbound to the ALB only from CloudFront edge IP ranges using security groups or ALB listener rules. Network ACLs are coarse and stateless and add operational burden. CloudFront does not have a “VPC origin” object; instead, CloudFront references the ALB DNS name. By limiting the ALB’s security group to CloudFront IP ranges, direct client access to the ALB is blocked while CloudFront remains permitted. This aligns with security best practices to “restrict origin access to only CloudFront” and use SGs for instance/ALB layer controls.

Amazon QuickSight includes built-in ML-powered forecasting capabilities, allowing you to create, visualize, and interact with time series forecasts directly within the BI dashboard, with no ML experience or infrastructure management required. This is the lowest management overhead solution.

AWS Documentation Extract:

"Amazon QuickSight provides built-in ML-powered forecasting, allowing business users to forecast future trends with a few clicks and no machine learning expertise required."

(Source: Amazon QuickSight documentation)

A, C, D: Require additional setup, management, or ML knowledge.

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.

Amazon DynamoDB supports TTL (Time To Live) for automated, scheduled deletion of expired items. It also offers point-in-time recovery (PITR) to restore the table to any second within the retention window (typically up to 35 days), providing full data durability and protection. DynamoDB can efficiently handle frequent updates and offers predictable performance. MemoryDB is an in-memory store, not designed for durable recovery. S3 with lifecycle policies does not handle updates as efficiently for small, frequent writes and is not as optimal for session data.

Reference Extract:

"DynamoDB supports TTL for automated expiration and deletion of items and PITR for continuous backups and restoration of data."

Source: AWS Certified Solutions Architect – Official Study Guide, DynamoDB section.

The company’s requirements are:

Store gigabytes of rarely accessed files daily.

Files must be available within minutes during the first year.

Retain files for 7 years cost-effectively.

The S3 Glacier Instant Retrieval storage class provides low-cost, long-term storage for data accessed occasionally, with millisecond retrieval time. This fits the requirement for availability within minutes during the first year. After one year, transitioning files to S3 Glacier Deep Archive (the lowest cost S3 storage class) with longer retrieval times is cost-effective for data retention over 7 years.

Option B uses S3 Standard and Glacier Flexible Retrieval, which is higher cost during the first year and slower retrieval times. Option C is less cost-efficient because EBS volumes are expensive for rarely accessed data and snapshots incur additional costs. Option D uses EFS, which is designed for low-latency file storage but is more expensive than S3 Glacier classes for long-term archival.

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.

ECS with Fargate: Allows containerized workloads to scale rapidly without managing underlying servers, handling unpredictable growth effectively.

RDS for Relational Data: Manages large relational datasets efficiently while supporting high availability.

SQS for Decoupling: Ensures message processing occurs in a specific order, decoupling application components and allowing independent evolution.

AWS ECS with Fargate Documentation,AWS SQS Documentation

Amazon EFS is a “fully managed, elastic, shared file system” that provides NFSv4 access across many EC2 instances and AZs, ideal for lift-and-shift of Linux NFS workloads. AWS DataSync “automates moving data between on-premises NFS servers and Amazon EFS,” performing incremental, parallel transfers with encryption and integrity checks, enabling cutover with minimal disruption. FSx for Lustre (A) targets HPC and S3-backed workloads, not general NFS shares. FSx for Windows (D) exposes SMB, not NFS. S3 (C) is object storage and not directly NFS-accessible by multiple EC2 hosts. EFS + DataSync satisfies NFS compatibility, multi-client access, and low-touch migration while remaining cost-effective for a 200-GB dataset.

Amazon Aurora MySQL–compatible offers a distributed, fault-tolerant storage system with Multi-AZ high availability and supports Aurora Replicas that “share the same underlying storage” for low-latency reads. Aurora provides Aurora Auto Scaling to “automatically add or remove Aurora Replicas based on load,” ideal for unpredictable, read-heavy workloads. This architecture offloads reads from the writer and maintains HA through automatic failover. Amazon RDS Single-AZ (B) lacks HA. Redshift (A) is a data warehouse, not a transactional DB. ElastiCache (D) can reduce read pressure but does not provide durable read replicas or automatic HA scaling at the database tier. Aurora’s design directly addresses the requirement to automatically scale reads while maintaining availability, matching Well-Architected guidance to use managed, elastic services for variable demand.

TheAWS_PROXY integration with Amazon API Gatewayallows the API to invoke a Lambda function synchronously, making it a suitable solution for the custom authorization mechanism and text translation use case.

Synchronous Invocation: The API Gateway REST API with AWS_PROXY integration enables synchronous processing of HTTP requests and responses, which is required for document translation.

Custom Authorization: API Gateway supports custom authorizers for fine-grained access control.

Lambda Function Execution: Although Lambda's execution time limit is 15 minutes, this is sufficient for the 6-minute document translation requirement.

Why Other Options Are Not Ideal:

Option B:

Introducing a Lambda function URL to invoke another Lambda function unnecessarily complicates the architecture.Not efficient.

Option C:

Asynchronous invocation cannot guarantee real-time response delivery for document translation tasks.Not suitable.

Option D:

Hosting the API on an EC2 instance increases operational overhead. HTTP PROXY integration does not add significant benefits here.Not cost-effective or efficient.

AWS References:

API Gateway Lambda Proxy Integration:AWS Documentation - Proxy Integration

Custom Authorization in API Gateway:AWS Documentation - Custom Authorization

CloudFront Signed Cookies:

CloudFront signed cookies allow the company to provide access to premium users while maintaining a single, consistent URL.

This approach is simpler and more scalable than managing presigned URLs for each file.

Incorrect Options Analysis:

Option A: Using EC2 and EBS increases complexity and cost.

Option B: Managing presigned URLs for each file is not scalable.

Option D: CloudFront signed URLs require unique URLs for each file, which does not meet the requirement for a single URL.

To upload photos to an S3 bucket using Amazon CloudFront with a custom domain name, the following components are required:

ACM in us-east-1 (Option A): When using CloudFront with HTTPS, the SSL/TLS certificate must be created in theus-east-1Region. AWS Certificate Manager (ACM) handles the provisioning, management, and renewal of public certificates, making this a cost-effective and low-maintenance solution.

S3 Transfer Acceleration (Option C): Transfer Acceleration allows faster uploads to S3 from CloudFront by routing traffic through AWS’s edge locations. This significantly speeds up the data upload process, especially for users that are geographically distant from the S3 bucket's region.

Option B (ACM in eu-west-1): CloudFront only supports certificates created in us-east-1.

Option D and E (OAC and website endpoint): These are not ideal for handling secure uploads or efficient data transfers in this case.

AWS References:

Using ACM with CloudFront

Amazon S3 Transfer Acceleration

This solution provides encryption at rest and in transit with the least operational overhead while adhering to the company’s security policies.

Encryption at Rest: Amazon RDS for MySQL can be configured to encrypt data at rest by using AWS Key Management Service (KMS) managed keys. This encryption is applied automatically to all data stored on disk, including backups, read replicas, and snapshots. This solution requires minimal operational overhead because AWS manages the encryption and key management process.

Encryption in Transit: AWS Certificate Manager (ACM) allows you to provision, manage, and deploy SSL/TLS certificates seamlessly. These certificates can be used to encrypt data in transit by configuring the MySQL instance to use SSL/TLS for connections. This setup ensures thatdata is encrypted between the application and the database, protecting it from interception during transmission.

Why Not Other Options?:

Option B (IPsec tunnels): While IPsec tunnels encrypt data in transit, they are more complex to manage and require additional configuration and maintenance, leading to higher operational overhead.

Option C (Third-party application-level encryption): Implementing application-level encryption adds complexity, requires code changes, and increases operational overhead.

Option D (VPN for encryption): A VPN solution for encrypting data in transit is unnecessary and adds additional complexity without providing any benefit over SSL/TLS, which is simpler to implement and manage.

AWS References:

Amazon RDS Encryption- Information on how to configure and use encryption for Amazon RDS.

AWS Certificate Manager (ACM)- Details on using ACM to manage SSL/TLS certificates for securing data in transit.

CloudTrail log file validation ensures that the log files have not been altered or deleted after delivery, providing an immutable audit log. Using KMS-managed encryption keys for CloudTrail log files adds another layer of data security, and AWS Config can monitor compliance to ensure this security is always enforced.

Reference Extract:

"CloudTrail log file validation provides assurance about the integrity of CloudTrail logs. Using SSE-KMS encryption and monitoring with AWS Config helps secure and audit logs for compliance."

Source: AWS Certified Solutions Architect – Official Study Guide, CloudTrail Security section.

A warm pool is an Auto Scaling feature that keeps instances in a pre-initialized state so they can quickly join the active group when scaling is required. This reduces the time needed for instance bootstrapping and makes new capacity available almost instantly. Option A only increases capacity limits but does not address slow boot times. Option C merely extends grace periods without solving the delay. Option D forces overprovisioning, which is wasteful and not aligned with cost optimization. Using a warm pool (B) directly addresses the problem by reducing response time to scaling events.

Detailed Explanation:

A. ECS + API Gateway:Overly complex and costly for an on-demand, intermittent workload.

B. EventBridge + SNS:EventBridge schedules are unnecessary for on-demand generation.

C. EKS + API Gateway:Overkill for this use case, with high operational overhead.

D. Lambda + SES:Most cost-effective and efficient solution for generating and emailing reports on demand.

AWS Transfer Family: This service provides fully managed support for file transfers directly into and out of Amazon S3 using the SFTP, FTPS, and FTP protocols.

SFTP Endpoints:

Set up an AWS Transfer Family server and configure SFTP endpoints to handle the file transfers.

This service is scalable and managed, reducing operational overhead compared to running an SFTP solution on EC2 instances.

Integration with Active Directory:

Choose the AWS Directory Service option as the identity provider for the Transfer Family server.

Use AD Connector to link the on-premises Active Directory with AWS, allowing employees to use their existing AD credentials to access the SFTP service.

Operational Efficiency: This solution leverages managed services for both file transfer and identity management, ensuring minimal changes to the current authentication mechanisms and reducing operational overhead.

When using an SQS queue as an event source for AWS Lambda, the visibility timeout of the SQS queue plays a critical role in preventing duplicate message processing.

"If your Lambda function doesn't process the message and delete it from the queue within the visibility timeout period, the message becomes visible again and can be processed again by the same or another function instance."

— AWS Lambda with SQS

In this scenario, the third-party API may take up to 60 seconds to respond. Since the Lambda function is configured with a 65-second timeout, the visibility timeout of the queue must be greater than or equal to the maximum function execution time to avoid the same message being reprocessed.

Incorrect Options:

A: Memory allocation doesn’t impact duplicate message handling.

B: Timeout is already sufficient; increasing it further does not solve the core issue.

C: Delivery delay controls initial delivery delay, not reprocessing logic.

Amazon Kinesis Data Firehose (now known as Amazon Data Firehose) supports near-real-time streaming, with built-in support to:

Invoke AWS Lambda functions for transformation.

Store data directly into Amazon S3 in desired formats like Apache Parquet.

“You can use Data Firehose to transform the data before delivering it, by invoking a Lambda function. You can convert to formats such as JSON or Apache Parquet before storing it into S3.”

— Amazon Data Firehose Developer Guide

Why not the others?

B: Kinesis Data Streams requires more operational overhead than Firehose.

C: DataSync is for file movement—not stream processing.

D: SQS isn’t designed for streaming and transformation pipelines.

Amazon Data Lifecycle Manager (DLM) automates the creation, retention, and deletion of EBSsnapshots. This allows organizations to define policies that ensure snapshots are only kept as long as needed, reducing costs automatically and minimizing manual effort. AWS recommends using DLM for optimizing storage and managing backup lifecycle with minimal overhead.

Option B:A gateway endpoint for S3 allows traffic to S3 without using public IPs and integrates with route tables.

Option D:Deploying EC2 instances in a private subnet with a NAT gateway enables outbound internet connectivity for other requirements without public IPs.

Option A:Egress-only internet gateways are for IPv6 traffic and do not work for IPv4 in this context.

Option C:Interface endpoints are not required for S3 as gateway endpoints are more suitable and cost-effective.

Option E:A customer gateway is for hybrid connectivity (e.g., on-premises), not suitable for this case.

AWS Documentation References:

VPC Endpoints

Amazon S3 Gateway Endpoints

AWS VPC endpoints (interface and gateway) allow private connectivity from VPC resources to AWS services without requiring public IP addresses or internet gateways. This ensures applications remain isolated in private subnets while securely accessing AWS services. NAT gateways (B) would allow internet access, which does not meet the security requirement. Internet gateways (C) directly expose traffic to the internet, which violates the isolation requirement. Direct Connect (D) connects on-premises environments to AWS but does not provide service access from private subnets. Therefore, option A — using interface VPC endpoints — is the correct solution.

Comprehensive and Detailed Explanation:

Amazon FSx for Lustre is a high-performance file system optimized for fast processing of workloads such as machine learning, high-performance computing (HPC), and video processing. It integrates natively with Amazon S3, allowing you to:

Access S3 Data: FSx for Lustre can be linked to an S3 bucket, presenting S3 objects as files in the file system.

High Performance: It provides sub-millisecond latencies, high throughput, and millions of IOPS, which are ideal for ML workloads.Amazon Web Services, Inc.

Minimal Operational Overhead: Being a fully managed service, it reduces the complexity of setting up and managing high-performance file systems.

Key Requirements:

Handle traffic spikes efficiently and reduce latency caused by cold starts.

Optimize costs during low traffic periods.

Analysis of Options:

Option A:

Provisioned Concurrency:Reduces cold start latency by pre-warming Lambda environments for the required number of concurrent executions.

AWS Application Auto Scaling:Automatically adjusts provisioned concurrency based on demand, ensuring cost optimization by scaling down during low traffic.

Correct Approach:Provides a balance between performance during traffic spikes and cost optimization during idle periods.

Option B:

Using EC2 instances with Auto Scaling introduces unnecessary complexity for a serverless architecture. It requires additional management and does not address the issue of cold starts for Lambda.

Incorrect Approach:Contradicts the serverless design philosophy and increases operational overhead.

Option C:

Setting a fixed concurrency level ensures performance during spikes but does not optimize costs during low traffic. This approach would maintain provisioned instances unnecessarily.

Incorrect Approach:Lacks cost optimization.

Option D:

Using EventBridge Scheduler for periodic invocations may reduce cold starts but does not dynamically scale based on traffic demand. It also leads to unnecessary invocations during idle times.

Incorrect Approach:Suboptimal for high traffic fluctuations and cost control.

AWS Solution Architect References:

AWS Lambda Provisioned Concurrency

AWS Application Auto Scaling with Lambda

Amazon RDSBlue/Green Deploymentsis the ideal solution for upgrading the database version with minimal operational overhead and no data loss. Blue/Green Deployments allows you to create a separate, fully managed "green" environment with the upgraded database version. You can test the new version in the green environment while the "blue" environment continuesserving production traffic. Once testing is complete, you can seamlessly switch traffic to the green environment without downtime.

This solution provides:

Fast, non-disruptive upgrade: Traffic is only switched to the new environment after testing, ensuring zero data loss.

Minimal operational overhead: AWS handles the infrastructure management, reducing manual intervention.

Option A (Manual snapshot): This requires manual intervention and involves more operational overhead.

Option B (Native backup/restore): This approach is more labor-intensive and slower than Blue/Green Deployments.

Option C (DMS): AWS DMS adds unnecessary complexity for a simple version upgrade when Blue/Green Deployments can handle the task more efficiently.

AWS References:

Amazon RDS Blue/Green Deployments

Enabling Multi-AZ deployment for an Amazon RDS DB instance is the simplest and most effective way to improve database availability and eliminate a single point of failure. Multi-AZ deployments provide automatic failover to a standby instance in case of a failure of the primary instance. This approach is managed by AWS and only requires a modification to the existing instance, with minimal manual intervention or downtime, thus providing the least implementation effort.

Reference Extract from AWS Documentation / Study Guide:

"With Multi-AZ deployments, Amazon RDS automatically creates a primary DB Instance and synchronously replicates the data to a standby instance in a different Availability Zone (AZ). In case of a failure of the primary DB Instance, Amazon RDS automatically fails over to the standby so that database operations can resume quickly."

Source: AWS Certified Solutions Architect – Official Study Guide, RDS High Availability section.

The requirements include:

Scalability: The solution must scale as video files are uploaded.

Long-running tasks: Processing tasks can take up to 30 minutes. AWS Lambda has a maximum execution time of 15 minutes, which rules out options that involve Lambda performing all the processing.

Serverless and event-driven architecture: Ensures cost-effectiveness and high availability.

Analysis of Options:

Option A:

AWS Lambda has a 15-minute timeout, which cannot support tasks that take up to 30 minutes.

EventBridge Scheduler is unnecessary for monitoring files when native event notifications are available.Not a valid choice.

Option B:

AWS Step Functions and AWS Fargate can handle long-running processes, but Amazon EFS is not the ideal storage for uploaded video files in a serverless architecture.

Processing tasks triggered by EFS events are not a common pattern and may introduce complexities.Not the best practice.

Option C:

Amazon S3 is used for storing uploaded files, which integrates natively with event-driven services like EventBridge and Step Functions.

Amazon S3 event notifications trigger a Step Functions workflow, which can orchestrate Fargate tasks to process large video files, meeting the scalability and execution time requirements.Correct choice.

Option D:

Similar to Option A, AWS Lambda cannot handle long-running processes due to its 15-minute timeout.

Invoking Lambda for processing directly is not feasible for tasks that take up to 30 minutes.Not a valid choice.

AWS References:

Amazon S3 Event Notifications:AWS Documentation - S3 Event Notifications

AWS Step Functions:AWS Documentation - Step Functions

AWS Fargate:AWS Documentation - Fargate

Comparison of Storage Services:AWS Storage Options

By leveragingAmazon S3,Step Functions, andFargate, this solution provides a scalable, efficient, and serverless approach to handling video processing tasks.

AWS Batch supports Windows-based jobs and automates provisioning and scaling of compute environments. Paired with AWS Fargate, it removes the need to manage infrastructure. This solution requires the least operational overhead and is cloud-native, providing flexibility and scalability.

Amazon RDS does not support enabling encryption at rest on an existing unencrypted DB instance. To encrypt an existing RDS instance’s data at rest, the recommended method is to:

Take a snapshot of the unencrypted DB instance.

Create an encrypted copy of the snapshot using AWS KMS. This encrypted snapshot contains the existing data encrypted at rest.

Restore a new DB instance from the encrypted snapshot. This new instance will have encryption at rest enabled.

Additionally, to manage encryption keys securely, companies can use customer managed keys (CMKs) in AWS Key Management Service (KMS). CMKs provide greater control over key management policies, rotation, and usage permissions compared to default AWS managed keys. Using a CMK allows customization of access control and auditability.

Option A is incorrect because you cannot enable encryption directly on a snapshot; you must create an encrypted copy. Option C is invalid because encryption cannot be enabled by modifying an existing instance’s configuration. Option D refers to the default AWS managed key, which is less flexible than customer managed keys.

Amazon CloudFront is a global content delivery network (CDN) that caches and distributes content to users with low latency. By setting the existing ALB as the origin, CloudFront can cache dynamic and static content closer to users worldwide, improving performance and reducing the load on the application servers. This is the most cost-optimized and AWS-recommended way to globally serve dynamic content for web applications.

Reference Extract:

"CloudFront accelerates delivery of both static and dynamic content, reducing latency and offloading origin resources by caching content at edge locations."

Source: AWS Certified Solutions Architect – Official Study Guide, CloudFront and Global Application section.

A. ElastiCache:Provides in-memory caching, not suitable for persistent, scalable databases.

B. DynamoDB Streams + Lambda:Manages replication manually, increasing latency and operational complexity.

C. Aurora Global Database:Provides high availability but does not support active-active configuration.

D. DynamoDB Global Tables:Provides active-active configuration and sub-second RPO.

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

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

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

AWS References:

IAM Roles for Amazon EC2explains how EC2 instances can use IAM roles to securely access AWS services without managing long-term credentials.

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

Why the other options are incorrect:

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

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

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

Amazon RDS read replicas provide a way to offload read traffic from the primary database, allowing read-intensive applications to query the replica without impacting the performance of the production (write) database. This is especially effective for workloads that involve frequently changing data but do not benefit from caching, since queries are rarely repeated.

Reference Extract from AWS Documentation / Study Guide:

"Read replicas allow you to elastically scale out beyond the capacity constraints of a single DB instance for read-heavy database workloads."

Source: AWS Certified Solutions Architect – Official Study Guide, RDS Read Replica section.

The Requester Pays feature in Amazon S3 allows the bucket owner to configure the bucket so that the requester, rather than the bucket owner, pays for data transfer and request costs. This is ideal for sharing large datasets with the public or with other AWS accounts when you want to minimize your own data transfer expenses.

Reference Extract from AWS Documentation / Study Guide:

"Requester Pays buckets allow you to configure the bucket so that the requester instead of the bucket owner pays the cost of the request and the data download from the bucket."

Source: AWS Certified Solutions Architect – Official Study Guide, S3 Cost Management section.

The optimal way to improve reliability and scalability of SFTP on AWS is to use AWS Transfer Family (for SFTP). It provides a fully managed SFTP server integrated with Amazon S3.

No EC2 instances or infrastructure management is required.

AWS Transfer Family supports custom DNS domains (e.g., sftp.example.com) and allows integration with existing authentication mechanisms like LDAP, AD, or custom identity providers.

Files are uploaded directly to S3, eliminating the need for cron jobs to move data from EC2 to S3.

Built-in high availability and scalability removes the burden of managing infrastructure.

Other options:

A and D still require manual scaling, server maintenance, and cron jobs.

C (Storage Gateway) is used for hybrid file access, not for replacing an SFTP server.

???? Reference:

AWS Transfer Family for SFTP

SSE-KMS: Server-side encryption with AWS Key Management Service (SSE-KMS) provides robust encryption of data at rest, integrated with AWS KMS for key management and auditing.

Automatic Key Rotation: By enabling automatic rotation for the KMS keys, the system ensures that keys are rotated annually without manual intervention, meeting compliance requirements.

Logging and Auditing: AWS KMS automatically logs all key usage and management actions in AWS CloudTrail, providing the necessary audit logs.

Implementation:

Create a KMS key with automatic rotation enabled.

Configure the S3 bucket to use SSE-KMS with the created KMS key.

Ensure CloudTrail is enabled for logging KMS key usage.

Operational Efficiency: This solution provides encryption, automatic key management, and auditing in a seamless, fully managed way, reducing operational overhead.

Comprehensive and Detailed Explanation:

To grant cross-account access to an Amazon S3 bucket, you can use a bucket policy that specifies the AWS account ID of the account you want to grant access to. This method allows Account B to access the S3 bucket in Account A without the need for additional services or configurations.

For the migration of data from an on-premises Oracle database to Amazon Aurora PostgreSQL, this solution effectively handles schema conversion, data migration, and ongoing data replication.

AWS Schema Conversion Tool (SCT): SCT is used to convert the Oracle database schema to a format compatible with Aurora PostgreSQL. This tool automatically converts the database schema and code objects, like stored procedures, to the target database engine.

AWS Database Migration Service (DMS): DMS is employed to perform the data migration. It supports both full-load migrations (for initial data transfer) and continuous replication of ongoing changes (Change Data Capture, or CDC). This ensures that any updates to the Oracle database during the migration are captured and applied to the Aurora PostgreSQL database, minimizing downtime.

Why Not Other Options?:

Option A (SCT + DMS full-load only): This option does not capture ongoing changes, which is crucial for a live database migration to ensure data consistency.

Option B (DataSync + S3): AWS DataSync is more suited for file transfers rather than database migrations, and it doesn’t support ongoing change replication.

Option D (Snowball + S3): Snowball is typically used for large-scale data transfers that don’t require continuous synchronization, making it less suitable for this scenario where ongoing changes must be captured.

AWS References:

AWS Schema Conversion Tool- Guidance on using SCT for database schema conversions.

AWS Database Migration Service- Detailed documentation on using DMS for data migrations and ongoing replication.

To make the application highly available across regions:

Deploy the application in a different region using a newECS clusterandALBto ensure regional redundancy.

UseRoute 53 failover routingto automatically direct traffic to the healthy region in case of failure.

UseDynamoDB Global Tablesto ensure the database is replicated and available across multiple regions, supporting read and write operations in each region.

Option D (EKS cluster in the same region): This does not provide regional redundancy.

Option E (Global Secondary Indexes): GSIs improve query performance but do not provide multi-region availability.

Option F (PrivateLink): PrivateLink is for secure communication, not for cross-region high availability.

AWS References:

DynamoDB Global Tables

Amazon ECS with ALB

The optimal solution for scalable and resilient hybrid networking is to use AWS Direct Connect with a Direct Connect gateway for secure, low-latency access to AWS, and an AWS Transit Gateway to manage connectivity among hundreds of VPCs.

By associating the Transit Gateway with the Direct Connect gateway, you enable transitive routing between on-premises and all VPCs, while minimizing network complexity and maintaining high performance.

VPC peering does not scale well, and VPNs don’t offer the same performance or consistency.

Comprehensive and Detailed Explanation:

Amazon EC2 On-Demand Instances: Since the batch jobs cannot be disrupted, On-Demand Instances provide the necessary reliability and availability.

Amazon S3 Standard: Storing data in S3 Standard for the first 30 days ensures quick and frequent access.

S3 Glacier Deep Archive: After 30 days, moving data to S3 Glacier Deep Archive significantly reduces storage costs for data that is rarely accessed.

S3 Lifecycle Configuration: Automating the transition and deletion of objects using lifecycle policies ensures cost optimization and compliance with data retention requirements.

The most cost-effective way to provide consistent SQL query performance on a heavily structured dataset, where some data is accessed more frequently, is to use Amazon Redshift as your main data warehouse for hot data and Amazon Redshift Spectrum to query cold data that remains in S3. With this approach, frequently queried data is loaded into Redshift for fast, consistent querying, while infrequently accessed data is left in S3 and accessed on demand via Spectrum, avoiding unnecessary data warehousing costs. Amazon Kinesis Data Firehose provides an easy and scalable way to ingest streaming data directly to both S3 and Redshift.

AWS Documentation Extract:

"Amazon Redshift Spectrum allows you to run queries against exabytes of data in Amazon S3 without loading or transforming the data. Load hot data into Redshift for fast access and query cold data in S3 with Spectrum, optimizing both cost and performance."

(Source: Amazon Redshift documentation, Spectrum)

A: Athena is good for ad hoc queries but not for consistent, high-performance SQL workloads.

B, C: Loading all data into Redshift is not cost-effective if some data is infrequently accessed.

A. Mutual TLS:Provides secure communication but does not integrate with AWS credential exchange.

B. AWS Signature v4:Requires direct integration with AWS and is less secure for external systems.

C. Kerberos:Not natively supported for AWS API authentication.

D. IAM Roles Anywhere:Enables AWS API access using X.509 certificates without long-term credentials.

This solution addresses the scalability needs of the workload while preventing failed processing attempts due to resource constraints.

Amazon S3 event notificationscan be used to trigger a message to an SQS queue whenever a new video is uploaded.

The existing Auto Scaling group of EC2 instances can poll the SQS queue, ensuring that the EC2 instances only process videos when there is a job in the queue.

SQS decouplesthe video upload and processing steps, allowing the system to handle sudden spikes in video uploads without overloading EC2 instances.

The use ofAuto Scalingensures that the EC2 instances can scale in or out based on the demand, maintaining cost efficiency while avoiding processing failures due to insufficient resources.

AWS References:

S3 Event Notificationsdetails how to configure notifications for S3 events.

Amazon SQSis a fully managed message queuing service that decouples components of the system.

Auto Scaling EC2explains how to manage automatic scaling of EC2 instances based on demand.

Why the other options are incorrect:

A. AWS Lambda functions: While Lambda can handle some workloads, video processing is often resource-intensive and long-running, making EC2 a more suitable solution.

B. Using SNS with Lambda: Similar to A, Lambda is not ideal for large-scale video processing due to its time and memory limitations.

D. AWS Step Functions: While a valid orchestration solution, this introduces more complexity and overhead compared to the simpler SQS-based solution.

The best practice for secure access control in AWS is to use IAM roles with least-privilege policies, granting only the permissions necessary to perform required tasks. Assigning roles individually ensures that developers cannot overstep their intended access boundaries.

Sharing credentials or using permanent access keys increases the risk of security breaches. Cognito is primarily intended for managing user access to applications, not AWS infrastructure. Thus, Option B best meets security and access control requirements.

Option Acombines ECS with Fargate for scalability, RDS for relational data, and SQS for decoupling with message ordering (FIFO queues).

Option Buses SNS, which does not maintain message order.

Option Cis suitable for serverless workflows but not relational data.

Option Drelies on Elastic Beanstalk, which offers less flexibility for scaling.

AWS advises using serverless event-driven processing to minimize operational burden and scale automatically with demand. Amazon SQS can be directly configured as an event source for AWS Lambda. With this setup, Lambda polls the queue, invokes the function, and processes messages without requiring the customer to manage infrastructure. Scaling is automatic, based on the volume of messages in the queue. Option A (increasing instance size) still leaves scaling and management challenges. Option B reduces cost when idle but does not address scaling. Option D requires manual triggering and does not meet continuous processing requirements. Migrating the script to Lambda (C) fully eliminates the need for instance management, providing scalability, reliability, and reduced operational overhead.

Amazon S3 Bucket Keys reduce the cost of AWS KMS API requests by generating a data key at the bucket level instead of individually calling KMS for every object read or written. This approach is particularly effective when workloads, such as ML pipelines, involve reading large numbers of encrypted objects. Switching to AWS managed keys (A) does not reduce the frequency of API calls. Removing encryption (B) would violate compliance/security requirements. Using CloudHSM (C) adds cost and operational burden. Therefore, the correct solution is D — enabling S3 Bucket Keys with SSE-KMS, which significantly reduces decryption costs while maintaining secure encryption.

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??.

AWS Backup is a fully managed backup service designed to centralize and automate data protection across AWS services including EC2 and RDS. It allows users to define backup schedules (backup plans) and automatically create and retain backups. AWS Backup also offers restore testing plans, allowing users to automate the validation of backups by restoring them in a controlled manner. This service is built to minimize operational overhead by removing the need to manage custom scripts, manual processes, or additional orchestration services. This aligns with AWS best practices for resilience, automation, and operational excellence.

Reference Extract from AWS Documentation / Study Guide:

"AWS Backup enables you to centralize and automate data protection across AWS services. You can create backup plans, schedule backups, and set lifecycle policies. AWS Backup also enables restore testing to verify your backup integrity, with minimal manual intervention."

Source: AWS Certified Solutions Architect – Official Study Guide, Resiliency and Disaster Recovery section; AWS Backup User Guide.

Amazon Managed Service for Apache Flink (formerly Kinesis Data Analytics) is a fully managed, serverless service for real-time processing of streaming data. You can consume data from Kinesis Data Streams, perform anomaly detection, and then output the results to another Kinesis stream. This approach is loosely coupled, fully managed, and does not require modifying the application code.

AWS Documentation Extract:

“Amazon Managed Service for Apache Flink enables you to process streaming data in real time, integrating with Kinesis Data Streams as source and sink, and is fully managed and serverless.”

(Source: Apache Flink on AWS documentation)

B: S3/Redshift is not real-time and adds complexity.

C, D: EC2/ECS solutions are not serverless or fully managed.

Amazon S3 Multi-Region Access Points allow applications to access S3 buckets in multiple Regions through a single global endpoint. This provides active-active read access with automatic routing to the closest bucket for low latency. With cross-Region replication, writes in the primary Region are automatically copied to the secondary Region, providing fault tolerance. Option A (CloudFront) provides caching and distribution, but does not address write replication or active-active bucket access. Option B (Transfer Acceleration) optimizes uploads across distances but does not enable cross-Region fault tolerance. Option C (AWS Backup) is designed for backup/restore, not real-time multi-Region reads and writes. Therefore, D is the correct solution for active-active read access and disaster recovery.

AWS Transfer Family is a fully managed service that provides SFTP, FTPS, and FTP access directly to Amazon S3. It allows organizations to support legacy data transfer protocols without managing infrastructure. This approach gives the vendors a familiar SFTP interface, with files landing directly in S3, and requires minimal operational effort compared to managing EC2 servers or using gateways.

Reference Extract from AWS Documentation / Study Guide:

"AWS Transfer Family enables secure transfer of files directly into and out of Amazon S3 using SFTP, FTPS, and FTP, and is fully managed by AWS, significantly reducing operational overhead."

Source: AWS Certified Solutions Architect – Official Study Guide, Data Migration and Transfer section.

The optimal solution for low-latency global caching with disaster recovery and cross-Region replication is to use Amazon ElastiCache Global Datastore for Redis OSS.

A Global Datastore enables fully managed cross-Region replication and supports automatic failover by promoting read replica clusters in another Region.

ElastiCache ensures encryption in-transit and at-rest, meeting compliance and security requirements.

It's a fully managed AWS-native feature, reducing operational effort compared to setting up DMS-based or snapshot-based replication manually.

Other options (A, C, D):

Require manual setup and management (e.g., custom DMS pipelines, snapshots).

Do not offer real-time replication or failover without manual intervention.

???? Reference:

ElastiCache Global Datastore for Redis

Option Ais the simplest solution, using DynamoDB Streams and Lambda for real-time ingestion into S3.

Options B, C, and Dadd unnecessary complexity with Data Firehose or Kinesis.

Amazon Timestreamis purpose-built for storing and analyzing time-series data like telemetry.

Option Aleverages Timestream, SageMaker for ML, and QuickSight for visualization, meeting all requirements with minimal complexity.

Option Binvolves more complex DynamoDB-EMR integration.

Option Cuses Neptune, which is designed for graph databases, not telemetry data.

Option Dincorrectly uses Athena for visualization instead of QuickSight.

To provide unique, secure URLs efficiently:

A: Create a wildcard custom domain in Route 53 as an alias for the API Gateway endpoint.

D: Request a wildcard certificate in ACM in the same Region as API Gateway (certificates must be in the same Region as the API).

F: Create a custom domain name in API Gateway and attach the certificate.

“You can configure a custom domain name for your API Gateway API. To use a wildcard certificate, request it from ACM in the same Region as your API.”

— API Gateway Custom Domain Names

This combination provides secure wildcard URLs without creating separate endpoints or hosted zones per user.

For a large-scale multi-account AWS environment with many VPCs and centralized Direct Connect, AWS recommends using a Transit Gateway (TGW) architecture combined with a Direct Connect gateway (DXGW). This setup allows scalable, centralized connectivity between on-premises and multiple VPCs across accounts.

Step B: Creating a Direct Connect gateway and Transit Gateway in a central network account and connecting them via a transit VIF enables the on-premises network to access all connected VPCs.

Step D: Sharing the transit gateway with other accounts via AWS Resource Access Manager (RAM) allows the central TGW to attach VPCs in multiple accounts, simplifying multi-account connectivity.

Step F: To route cloud resources’ internet traffic back through the on-premises data center (for centralized egress), provisioning only private subnets and routing outbound internet traffic through NAT or firewall services in the data center is necessary. This requires configuring transit gateway and customer gateway routes appropriately.

Option A is partially correct in the use of Direct Connect gateway but association proposals are not scalable for hundreds of VPCs and accounts compared to transit gateway. Option C (internet gateway) is irrelevant here as traffic egress is required via on-premises data center, not directly to the internet. Option E (VPC peering) is not scalable for hundreds of VPCs.

Comprehensive and Detailed Explanation:

AWS Control Tower provides a straightforward way to set up and govern a secure, multi-account AWS environment based on AWS best practices. It automates the setup of a baseline environment, or landing zone, that includes:

Service Control Policies (SCPs): These are used to manage permissions across AWS Organizations, allowing you to set permission guardrails. SCPs can restrict access to specific AWS services and actions, helping enforce security best practices.

Multi-Factor Authentication (MFA): AWS Control Tower can enforce MFA for the root user across all accounts, enhancing security.

Centralized Governance: It offers centralized logging and monitoring, making it easier to manage and audit multiple AWS accounts.

A VPC gateway endpoint for Amazon S3 enables private connectivity to S3 without routing traffic through a NAT gateway or over the internet, eliminating NAT gateway costs. This solution is secure and redundant, as S3 endpoints are highly available by design.

AWS Documentation Extract:

"A gateway VPC endpoint enables you to privately connect your VPC to supported AWS services without requiring a NAT gateway or internet gateway."

(Source: Amazon VPC documentation, Gateway Endpoints)

A: NAT instances still incur operational overhead and costs.

B: Internet gateway exposes resources and does not provide private access.

D: Direct Connect is for hybrid networking, not for cost-efficient S3 access.

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

Key AWS references and benefits ofS3 Intelligent-Tiering:

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

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

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

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

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.

DynamoDB Streams: Captures real-time changes in DynamoDB tables and allows integration with Lambda for processing the changes.

Minimal Operational Overhead: Using a Lambda function directly to write data to S3 ensures simplicity and reduces the complexity of the pipeline.

Amazon DynamoDB Streams Documentation

Amazon S3 Storage Lens:

S3 Storage Lens provides organization-wide visibility into object storage usage and activity trends.

It can generate metrics and insights about your S3 buckets, including versioning status.

Configuration:

Enable S3 Storage Lens at the organization level.

Configure the dashboard to include the versioning status metric.

Identify Non-Versioned Buckets:

Use the S3 Storage Lens dashboard to filter and identify buckets that do not have versioning enabled.

Storage Lens provides detailed insights and reports which can be used to enforce compliance and manage storage effectively.

Operational Efficiency: Using S3 Storage Lens provides a centralized, easy-to-use interface for monitoring bucket configurations across multiple Regions and accounts, reducing the need for custom scripts or manual checks.

A. Edge-optimized API + Caching:Reduces latency by using Amazon CloudFront for edge locations and enables caching for dynamic content. Compression reduces data transfer latency.

B. Regional API + Caching:Increases latency for global users due to the lack of edge locations.

C. Edge-optimized API + Reserved Concurrency:Reserved concurrency ensures Lambda availability but does not address latency for dynamic content.

D. Regional API + Reserved Concurrency:Lacks edge optimization, increasing latency for global users.

Amazon EventBridge supports routing application-generated events to AWS Lambda targets. The Lambda function can process and insert events into a DynamoDB table. This is a standard design pattern for connecting event-driven applications with DynamoDB. Option A confuses DynamoDB Streams (which streams changes from DynamoDB) with EventBridge (which is for event routing). Options C and D reference partner event buses, which are intended for SaaS integrations, not custom application events. Therefore, the correct and simplest solution is to use a custom EventBridge event bus with a Lambda target (B).

The most scalable and managed solution for streaming ingestion, real-time transformation, and delivery to Amazon S3 is Amazon Kinesis Data Streams, Amazon Managed Service for Apache Flink, and Amazon Kinesis Data Firehose.

From AWS Documentation:

“Amazon Kinesis Data Streams enables real-time processing of streaming data at massive scale. With Apache Flink on Kinesis Data Analytics, you can process data streams in near real-time, then use Amazon Kinesis Data Firehose to reliably deliver that data to S3.”

(Source: Amazon Kinesis Developer Guide)

Why A is correct:

Fully managed: All services involved are serverless and managed.

Real-time ingestion: Kinesis Data Streams supports sub-second latency and can handle high-throughput workloads like 100,000+ devices.

Near real-time processing: Apache Flink is designed for continuous stream processing with complex event handling.

Efficient delivery: Kinesis Firehose delivers processed data directly to S3 with retry and backup capability.

Why other options are incorrect:

Option B: SQS is not optimized for real-time streaming at high volume.

Option C: EC2 + Kafka + EMR adds high operational overhead and cost.

Option D: EventBridge is event-driven, not designed for high-throughput streaming; AWS Batch is unsuitable for near real-time processing.

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.

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

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

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

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

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

AWS References:

Amazon SQS FIFO Queues

Decoupling Application Tiers Using Amazon SQS

AWS recommends Auto Scaling with dynamic scaling policies to handle unpredictable workload spikes. Target tracking scaling policies automatically adjust capacity based on defined metrics such as average CPU utilization or request count per target. This approach ensures applications maintain responsiveness without overprovisioning. Scheduled scaling (options B and C) is only effective when traffic patterns are predictable, which is not the case here. Reserved Instances or Spot Instances also do not address sudden demand changes effectively. Replacing the ALB with an NLB (D) does not solve latency caused by EC2 instance capacity. Therefore, using EC2 instances in an Auto Scaling group with a target tracking scaling policy is the most effective and cost-efficient solution for unpredictable, short-lived traffic spikes.

Export Requirements:

To export data from DynamoDB to Amazon S3, point-in-time recovery (PITR) must be enabled for the tables. This feature creates a snapshot of the data, which is essential for exports.

Incorrect Options Analysis:

Option B: S3 buckets and DynamoDB tables do not need to be in the same region for exports.

Option C: DynamoDB streams are unrelated to the export functionality.

Option D: DAX accelerates reads but has no role in exports.

Amazon RDSMulti-AZ DB cluster deploymentensures high availability by automatically replicating data across multiple Availability Zones (AZs), and it supports failover in case of a failure in one AZ. This setup also provides increased capacity for read workloads by allowing read scaling with reader instances in different AZs. This solution offers the most operational efficiency with minimal manual intervention.

Option A (DynamoDB): DynamoDB is not suitable for a relational database workload, which requires a PostgreSQL engine.

Option B (RDS with Multi-AZ): While this provides high availability, it doesn't offer read scaling capabilities.

Option D (Cross-Region Read Replicas): This adds complexity and is not necessary if the requirement is high availability within a single region.

AWS References:

Amazon RDS Multi-AZ DB Cluster

The requirement is secure, permanent connectivity to two VPCs without enabling VPC-to-VPC communication. Option B achieves this by establishing a separate Site-to-Site VPN connection between the remote office and each VPC. Updating each VPC’s route tables ensures traffic from the remote office reaches the correct VPC resources, while preventing routing between the VPCs themselves. Options involving transit gateways (A and D) would introduce transitive routing capabilities, which violates the requirement of isolation between VPCs. Option C (VPC peering) directly conflicts with the requirement by enabling cross-VPC access. Therefore, option B is the correct solution as it maintains isolation while providing secure VPN connectivity.

AWS Firewall Manager integrates with AWS Organizations to centrally manage and apply security group policies, AWS WAF rules, and AWS Shield Advanced protections. It automates the propagation of rules across accounts and Regions and can also audit and remediate noncompliant configurations.

Field-level encryption in Amazon CloudFront provides end-to-end encryption for specific data fields (e.g., credit card numbers, social security numbers). It ensures that sensitive fields are encrypted at the edge before being forwarded to the origin, and only the authorized application with the private key can decrypt them.

This adds a layer of protection beyond HTTPS, which encrypts the whole payload but not individual fields. Signed URLs and cookies are for access control, not encryption. Setting HTTPS Only is a good practice but does not satisfy the field-specific encryption requirement.

TooManyRequestsException errors occur when Lambda exceeds concurrency limits. The recommended pattern is to use Amazon SQS with Lambda to decouple and buffer traffic, ensuring that bursts of requests are queued and processed smoothly. Enabling provisioned concurrency for Lambda ensures that functions are pre-initialized and ready to handle spikes in load with low latency. Step Functions (B) is designed for workflow orchestration, not high-throughput request buffering. SNS with Lambda (C) does not provide buffering and may overwhelm Lambda during bursts. Reserved concurrency (D) limits function scaling instead of improving resilience. Therefore, option A provides a scalable and resilient solution, minimizing errors during traffic surges.

Amazon S3 is the most scalable and cost-optimized object storage for large amounts of data. It integrates natively with Amazon SageMaker, which allows direct access to S3 data for machine learning training jobs without the need to copy data elsewhere. The S3 Intelligent-Tiering storage class further optimizes storage costs for data with unknown or changing access patterns, while maintaining immediate availability for ML training.

Copying to FSx for Lustre or EFS adds unnecessary complexity and cost unless ultra-high throughput POSIX file access is specifically required, which is not mentioned here.

AWS Documentation Extract:

"Amazon S3 provides cost-effective storage for ML data sets, and Amazon SageMaker can directly access data in S3. S3 Intelligent-Tiering automatically moves data between frequent and infrequent access tiers, optimizing costs for changing data access patterns."

(Source: AWS S3 and SageMaker documentation)

A, D: FSx for Lustre or EFS may be used in specialized cases but are not cost-optimized for general large-scale ML data storage.

C: SageMaker notebook instance storage is not designed for large-scale data ingestion or long-term storage.

AWS Shield Advanced provides:

Advanced DDoS detection and mitigation

24/7 access to the AWS DDoS Response Team (DRT)

Real-time metrics and alerts via CloudWatch

Integrated with CloudFront, ALB, Route 53, and Global Accelerator

“Shield Advanced provides enhanced detection and mitigation for more sophisticated DDoS attacks and gives you access to the AWS DDoS Response Team (DRT).”

— AWS Shield Advanced Overview

Incorrect Options:

A (AWS WAF): For application-layer filtering only.

B (Shield Standard): Basic protection, no DRT or attack visibility.

C (Macie): Used for discovering sensitive data in S3, unrelated to DDoS.

If the SQS visibility timeout is set shorter than the time it takes for the application to process and delete the message, the message becomes visible to other consumers and can be processed again, resulting in duplicate processing. This is a common cause of duplicate messages when using SQS.

Reference Extract from AWS Documentation / Study Guide:

"If the visibility timeout for a message is set shorter than the time it takes to process the message, the message becomes visible again and can be received and processed again, resulting in duplicate processing."

Source: AWS Certified Solutions Architect – Official Study Guide, SQS and Messaging section.

AWS Fargate is a serverless compute engine for containers that works with Amazon EKS. With Fargate, you do not need to provision or manage EC2 instances or clusters. You simply define and deploy your pods, and Fargate automatically launches the required compute resources. This results in the lowest operational overhead because AWS manages the infrastructure. Fargate profiles allow you to specify which pods run on Fargate.

AWS Documentation Extract:

“AWS Fargate is a serverless, pay-as-you-go compute engine that lets you focus on building applications without managing servers. With Amazon EKS and Fargate, you only need to define your application’s pods; Fargate provisions and manages the required compute resources for you.”

(Source: Amazon EKS documentation, AWS Fargate integration)

Other options:

A: Self-managed nodes require you to manage EC2 instances.

B: Managed node groups reduce some overhead, but you are still responsible for patching and managing the EC2 instances.

D: Managed node groups with Karpenter automate scaling but do not remove the need to manage underlying instances.

According to AWS best practices, each tier’s security group must restrict inbound traffic to only the upstream trusted source. For the web tier, the Application Load Balancer must be the only entity allowed to send traffic. AWS documentation specifies: “Restrict the security groups associated with your targets to accept traffic only from the load balancer.” This confirms that the web tier security group should allow inbound HTTPS from the ALB security group (A).

For communication between the web and application tiers, AWS states: “You can specify a security group as the source or destination in a rule” and “Create rules only for the protocols and ports required by your application.” Therefore, the application tier security group must allow inbound HTTPS traffic from the web tier security group (E).

For the database tier, AWS guidance says: “Allow only the necessary ports for database communication.” Microsoft SQL Server listens on port 1433 by default, so the database tier security group must allow inbound SQL Server traffic from the application tier security group (C).

Outbound rules (options B, D, and F) are unnecessary because AWS specifies that “Security groups are stateful. Return traffic is automatically allowed.” This means once inbound rules are defined, the return path is automatically permitted without extra outbound configurations.

This combination (A, C, E) applies the principle of least privilege, ensures end-to-end secure communication across tiers, and follows AWS recommendations for ALB-to-target security group setups.

This solution leverages:

Amazon Redshift Spectrum to query S3 data directly from Redshift.

Amazon Athena for ad-hoc analysis of S3 data.

Amazon QuickSight for unified visualization from multiple data sources.

“Redshift Spectrum enables you to run queries against exabytes of data in Amazon S3 without having to load or transform the data.”

“QuickSight supports both Amazon Redshift and Amazon Athena as data sources.”

— Redshift Spectrum

— Amazon QuickSight Supported Data Sources

This architecture allows scalable querying and visualization with minimum ETL overhead, ideal for BI dashboards.

Incorrect Options:

A: The query editor is not a BI tool.

B, D: Grafana is better for time-series data, not structured analytics or BI reports.

To enforce that IAM users can only access Amazon RDS and no other AWS services, the recommended approach is to use a Deny statement with NotAction. This ensures that all actions are denied except RDS actions. Options A and B do not fully achieve the restriction: A only allows RDS but does not explicitly deny access to other services if another policy grants access; B’s explicit Deny for “*” would override all other permissions, including the intended RDS Allow, which would result in no access at all. Option D with permissions boundaries still allows other attached policies to grant access outside RDS. Therefore, C is the correct approach to enforce RDS-only access.

For infrequent or ad hoc querying of log data, Amazon S3 + Amazon Athena provides the most cost-effective, serverless, and scalable analytics solution.

From AWS Documentation:

“Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.”

(Source: Amazon Athena User Guide)

Why A is correct:

Amazon S3 offers durable, scalable, and cost-efficient storage.

Athena allows SQL-based querying on structured or semi-structured data like logs.

No need to provision or manage infrastructure.

Ideal for occasional querying at low cost.

Why the others are not optimal:

Option B: OpenSearch adds cost and is best for frequent, low-latency log querying.

Option C: RDS is not optimized for large-scale write-heavy log ingestion and costs more.

Option D: CloudWatch Logs is suitable for real-time monitoring, not for long-term storage and analytics of large log volumes.

The requirements specify capturing unpredictable and sudden spikes in customer activity, integrating easily with other web applications, and including authorization.

Amazon API Gateway with Lambda authorizer provides a secure, scalable entry point with flexible authorization mechanisms including token validation.

Amazon Kinesis Data Firehose is a fully managed service to reliably load streaming data into destinations such as Amazon S3, which fits well for capturing streaming customer activity data.

API Gateway integrates natively with Firehose for direct ingestion.

This combination supports unpredictable traffic, smooth scaling, and simple authorization.

Option B uses Kinesis Data Streams, which requires more management than Firehose and is less optimized for direct API integration. Options A and D use Gateway Load Balancer and ECS containers plus EFS, which add complexity and are less suited for unpredictable traffic with integrated authorization.

AWS CloudTrail Lakeis a fully managed service that allows the collection, storage, and querying ofCloudTrail eventsfor both AWS and non-AWS services. CloudTrail Lake can be customized to collect logs from various sources, ensuring a centralized audit solution. It also supports long-term storage, so logs can be retained for 7 years, meeting the compliance requirement.

Option A (Data Lake): Setting up a data lake in S3 introduces unnecessary operational complexity compared to CloudTrail Lake.

Option C (Ingest non-AWS services into CloudTrail): CloudTrail Lake is better suited for this task with less operational overhead.

Option D (CloudWatch Logs): While CloudWatch can store logs, CloudTrail Lake is specifically designed for API auditing and storage.

AWS References:

AWS CloudTrail Lake

Amazon Aurora Serverless is a fully managed, MySQL-compatible database that automatically scales based on demand and provides high availability. Combining this with EC2 Auto Scaling and an Application Load Balancer achieves both application and database high availability and scalability.

Reference Extract:

"Aurora Serverless automatically starts up, shuts down, and scales capacity based on your application's needs, providing a cost-effective, highly available database solution."

Source: AWS Certified Solutions Architect – Official Study Guide, Aurora Serverless and Scaling section.

AWS guidance for NAT Gateway recommends deploying “a NAT gateway in each Availability Zone and configure your routing to ensure that resources use the NAT gateway in the same Availability Zone.” This provides “zone-independent architecture” and avoids cross-AZ data processing charges and single-AZ failures. Option B creates a single point of failure and incurs cross-AZ egress charges when private subnets in other AZs traverse a centralized NAT. NAT instances (C) are legacy, require manual scaling/failover/patching, and are not recommended for production HA. Option D is not supported (NLB cannot front a NAT Gateway as a target). With steady, uniform load, per-AZ NAT Gateways deliver high availability with predictable cost; routing each private subnet to its local NAT Gateway maintains security (no inbound initiated connections) and resilience. This meets the requirement for cost-effective, secure outbound connectivity across multiple AZs while preserving availability.

Converting the existing DynamoDB table to aglobal tableprovides active-active replication and low-latency reads and writes in both Regions. DynamoDB global tables are specifically designed for multi-Region and multi-active use cases.

Option A:GSIs do not provide multi-Region replication or active-active capabilities.

Option B and D:Using DynamoDB Streams and custom replication is less operationally efficient than global tables and introduces additional complexity.

AWS Documentation References:

DynamoDB Global Tables

Amazon EC2 allows you to install and manage SQL Server directly on a Windows or Linux VM, giving you full access to the underlying operating system. This is required when you need to install custom agents, configure OS-level features, or have complete control over the environment.

Amazon RDS and Aurora are managed services and do not provide access to the underlying OS, nor does Redshift (which is a data warehouse, not SQL Server).

AWS Documentation Extract:

"If you require access to the underlying operating system or need to install custom software, use SQL Server on Amazon EC2. Amazon RDS is a managed service and does not provide OS-level access."

(Source: AWS Database Migration documentation, SQL Server Deployment Options)

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.

AmazonKinesis Data Streamsis the best choice for this scenario:

Message throughput: Kinesis Data Streams supports high throughput with enhanced fan-out and dedicated throughput for consumers.

Large message size: Supports message sizes up to 1 MB, meeting the 500 KB requirement.

Message retention: Data streams can retain messages for up to 365 days.

Strict ordering: Guarantees message ordering within shards.

Why Other Options Are Not Ideal:

Option B:

While SQS FIFO supports strict ordering, SNS topics do not. SNS also does not natively support message retention or strict ordering across consumers.Does not meet requirements.

Option C:

EventBridge does not provide strict ordering guarantees or message retention beyond 24 hours.Does not meet requirements.

Option D:

SNS topics with Data Firehose are not designed for use cases requiring strict ordering or long message retention.Does not meet requirements.

AWS References:

Amazon Kinesis Data Streams:AWS Documentation - Kinesis Data Streams

AWS Messaging Services Comparison:AWS Documentation - Messaging Services

Amazon FSx for Lustreis a high-performance, fully managed file system that is ideal for applications requiring low-latency access to shared storage, especially in use cases like gaming where high throughput and low latency are essential. It integrates easily with EC2 instances, providing fast and scalable shared storage, and supports custom protocols for specific application needs.

Option A (FSx File Gateway): FSx File Gateway is designed for hybrid cloud storage and is not suited for high-performance gaming workloads.

Option B (EC2 Windows instance): Setting up a file share on a Windows instance would introduce additional administrative overhead and would not provide the necessary performance.

Option C (EFS with Lustre): While Lustre is integrated with FSx, EFS does not natively support Lustre.

AWS References:

Amazon FSx for Lustre

Comprehensive and Detailed Explanation:

For a MySQL database experiencing high write operations, using Amazon RDS with Provisioned IOPS (io1 or io2) SSD storage is recommended to achieve consistent and low-latency performance. Provisioned IOPS allows you to specify a desired IOPS rate, which is crucial for write-intensive workloads.

Monitoring write operation metrics through Amazon CloudWatch enables you to observe performance and adjust the provisioned IOPS as needed to meet application demands.

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

AWS WAF (Web Application Firewall):

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

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

Configuration:

Deploy your APIs using Amazon API Gateway.

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

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

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

Partition Key Issue:

Using "service name" as the partition key results in uneven data distribution. Some shards may become hot due to excessive logs from certain services, leading to throttling errors.

Changing the partition key to "creation timestamp" ensures a more even distribution of records across shards.

Incorrect Options Analysis:

Option A: On-demand capacity mode eliminates throughput management but is more expensive and does not address the root cause.

Option B: Adding more shards does not solve the issue if the partition key still creates hot shards.

Option D: Using separate streams increases complexity and is unnecessary.

Amazon EFS provides a shared, elastic, low-latency file system that can be mounted concurrently by many EC2 instances across multiple Availability Zones, delivering strong read-after-write consistency so all instances see updates almost immediately. This is the standard pattern for CMS-style workloads that require shared, up-to-date assets with minimal lag. Syncing local copies from S3 (C) introduces polling windows and eventual consistency delays; hourly sync is not near-real time. Copying from a “newest instance” (A) is brittle and not scalable. EBS volumes/snapshots (D) are single-instance, single-AZ block devices and not designed for multi-writer sharing across instances/AZs. EFS’s multi-AZ design and POSIX semantics provide the simplest, most reliable solution with the least operational overhead.

Amazon Route 53 supports failover routing policies, which use health checks to route DNS queries to a secondary Region only if the primary endpoint fails. This design ensures only one Region is active for traffic at any given time. This is the recommended architecture for active-passive, multi-Region DR strategies.

AWS Documentation Extract:

“Failover routing lets you route traffic to a primary resource, such as a web server in one Region, and a secondary resource in another Region. If the primary fails, Route 53 can route traffic to the secondary resource automatically.”

(Source: Amazon Route 53 documentation, Routing Policy Types)

A, D: These options do not configure DNS failover for external users.

C: Geolocation routing is for regional distribution, not DR failover.

To securely access AWS services like S3 and Secrets Manager from a private VPC without using public IPs, interface VPC endpoints are required. These endpoints are accessible via private IP addresses. For the application to reach these endpoints, appropriate routes must be configured in the route table.

Aurora Global Database: Provides cross-Region disaster recovery with minimal data loss (<1 second replication latency).

S3 Cross-Region Replication (CRR): Automatically replicates data between buckets in different Regions.

“Aurora Global Database replicates your data with typically under one second of latency to secondary Regions.”

“Amazon S3 Cross-Region Replication automatically replicates objects across buckets in different AWS Regions.”

— Aurora Global Database

— S3 Cross-Region Replication

This meets the multi-Region DR requirement with minimal data loss.

Using API Gateway and Lambda enables serverless handling of form submissions with minimal cost and infrastructure. When coupled with Amazon SNS, it allows instant email notifications without running servers, making it ideal for low-traffic workloads.

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

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

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

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

AWS References:

Amazon S3 for Media Storage

AWS Global Accelerator improves the performance and availability of applications by directing user traffic through the AWS global network of edge locations using anycast IP addresses. It reduces latency and jitter for global users accessing applications in a single Region.

Why this works:

Global Accelerator routes user requests to the nearest AWS edge location using AWS’s high-performance backbone network.

It then forwards traffic to the optimal endpoint — in this case, the public API hosted on EC2.

This is much more cost-effective and requires less operational complexity than deploying and maintaining multiple API Gateway endpoints across regions (Option B), or setting up Direct Connect links for every international location (Option A).

Option C requires no application change and is designed specifically for latency improvement and high availability.

???? References:

AWS Global Accelerator Documentation

Use Cases for Global Accelerator

Performance Improvements for Global Users

Lambda@Edge allows you to run functions at CloudFront edge locations, enabling modifications to content before it's delivered to users, including compression — which directly reduces data transfer cost.

“Lambda@Edge can be used to compress or modify your content before delivering it to end users, which reduces the amount of data transferred.”

— Lambda@Edge Use Cases

Since code modifications aren’t allowed, using Lambda@Edge is a non-invasive way to:

Compress responses.

Reduce transfer size = lower CloudFront cost.

Incorrect Options:

B: S3 Transfer Acceleration improves speed, not cost.

C: Caching doesn’t help if content is always unique (single-use).

D: Multipart uploads help with large file uploads, not transfers.

The company wants to move from an on-premises Docker environment to fully managed AWS services with persistent storage. The best fit is:

Amazon ECS with AWS Fargate launch type: This is a serverless container orchestration solution where AWS manages the underlying infrastructure, removing the need to manage EC2 or Kubernetes nodes.

Amazon EFS (Elastic File System): This is a fully managed, scalable, and shared file system for use with ECS tasks. It supports persistent storage for containers, replacing the local volumes used on-premises.

This combination (ECS + Fargate + EFS) is fully managed and requires no manual server maintenance.

Option A uses EKS with self-managed nodes, which is not fully managed.

Option C (DynamoDB) is for structured key-value storage, not for persistent file storage.

Option D uses ECS with EC2 launch type, which is not serverless and requires managing instances.

???? Reference:

Using Amazon ECS with AWS Fargate

Mounting EFS volumes in ECS tasks

Amazon DynamoDB supports both TTL (Time to Live) for automatic deletion and Point-in-Time Recovery (PITR) for backup and restore of table data. It's ideal for frequently updated data with short retention requirements and minimal operational overhead.

TTL attribute ensures expired items are automatically removed.

PITR enables recovery from accidental deletes or application errors.

S3 Intelligent-Tiering is designed for data with unknown or changing access patterns. It automatically moves objects between frequent and infrequent access tiers as needed, with no retrieval fees for accessing data in any tier, and no performance impact. Minimal management overhead is required because AWS manages all transitions automatically. This class is cost-optimized and meets all requirements listed.

AWS Documentation Extract:

"S3 Intelligent-Tiering is the only storage class that automatically moves data between frequent and infrequent access tiers when access patterns change, with no retrieval charges and no impact on performance. It is designed to optimize costs automatically when data access patterns are unpredictable."

(Source: Amazon S3 documentation, Intelligent-Tiering storage class)

Other options:

B: Storage class analysis only provides recommendations, not actual storage tiering.

C & D: S3 Standard-IA and S3 One Zone-IA both have retrieval fees and may impact retrieval time and data durability.

For maximum resiliency and fault tolerance in a mission-critical scenario, AWS recommends redundant Direct Connect connections from multiple data centers to multiple AWS Direct Connect locations.

This protects against:

Data center failure

Device failure

Location outages

“For workloads that require high availability, we recommend that you use multiple Direct Connect connections at multiple Direct Connect locations.”

— AWS Direct Connect Resiliency Recommendations

Option C follows AWS maximum resiliency model.

AWS Backup supports cross-Region backup for Amazon EFS, allowing automated, scheduled backups and replication to another Region. This managed service simplifies backup management and ensures data resilience without the need for custom scripts or manual processes

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.

AWS RDS Proxy is a fully managed, highly available database proxy that allows applications to pool and share database connections efficiently.

In serverless architectures like Lambda, rapid invocations can open numerous concurrent connections to Aurora, potentially overwhelming the database and causing “too many connections” errors.

By using Amazon RDS Proxy, the solution:

Pools database connections.

Maintains warm connections that can be reused.

Supports IAM authentication and Secrets Manager integration.

Requires minimal application change and low operational effort.

This directly supports the Performance Efficiency pillar of the AWS Well-Architected Framework, ensuring the application scales without overloading the DB.

???? Reference:

Amazon RDS Proxy Documentation

Lambda + RDS Best Practices

To improve availability and fault tolerance of an Amazon RDS instance, the recommended approach is to configure a Multi-AZ deployment.

Multi-AZ deployments for RDS automatically replicate data to a standby instance in a different Availability Zone (AZ).

If a failure occurs in the primary AZ (due to hardware, network, or power), RDS will automatically failover to the standby instance with minimal downtime, without administrative intervention.

This is an AWS-managed feature and does not require application modification.

It does not provide scalability or load balancing; it's designed for high availability and resiliency.

Options A, B, and D are incorrect:

A refers to cross-Region, which is used for disaster recovery, not high availability.

B and D with SQS do not address high availability directly for the RDS instance; queues help decouple systems but do not make a database more resilient.

???? Reference:

Amazon RDS Multi-AZ Deployments

This solution meets the company's requirements with minimal administrative overhead and ensures security and ease of management.

AWS AppConfig: AWS AppConfig is a service designed to manage application configuration in a secure and validated way. It allows you to deploy configurations safely and quickly without affecting the application's performance or availability.

AWS Secrets Manager: AWS Secrets Manager is specifically designed to manage, retrieve, and rotate credentials for databases and other services. It integrates seamlessly with AWS services like Amazon RDS, making it an ideal solution for securely storing and retrieving database credentials. Secrets Manager also provides automatic rotation of credentials, reducing the operational burden.

Why Not Other Options?:

Option B (AWS Lambda + Parameter Store): While AWS Lambda can be used for managing configurations and AWS Systems Manager Parameter Store can store credentials, this approach involves more manual setup and does not offer the same level of integrated management and security as AppConfig and Secrets Manager.

Option C (Encrypted S3 Configuration File): Storing configuration and credentials in S3 files involves more manual management and security considerations, increasing the administrative overhead.

Option D (AppConfig + RDS for credentials): RDS is not designed for storing application credentials; it's better suited for managing database instances and their configurations.

AWS References:

AWS AppConfig- Describes how to use AWS AppConfig for managing application configurations.

AWS Secrets Manager- Provides details on securely storing and retrieving credentials using AWS Secrets Manager.

Amazon CloudFront is a highly cost-effective CDN that caches content like images and videos at edge locations globally. This reduces latency and the load on the origin S3 bucket. It is ideal for static content that is accessed by many users.

Amazon ECS with AWS Fargate provides serverless container compute that can scale tasks dynamically in response to demand. This matches the unpredictable scaling requirements of a gaming workload. Aurora Serverless v2 provides on-demand, autoscaling relational database capacity while supporting complex SQL queries. EC2 with Auto Scaling (A) works but requires significant management overhead. Lambda with DynamoDB (B) is not suitable because the workload requires a relational database and long-running processes. ParallelCluster with HPC (D) is designed for batch scientific workloads, not dynamic, interactive gaming. Option C provides the correct balance of elasticity, performance, and managed services for both compute and relational database needs.

EC2 Auto Scaling warm pools allow you to pre-initialize instances, reducing the delay in scale-out events. This results in significantly faster response times during demand surges while remaining cost-effective compared to always running at peak capacity.

The SELECT INTO OUTFILE S3 feature allows you to export Amazon Aurora MySQL data directly to Amazon S3 with minimal operational overhead. This method is efficient and cost-effective for archiving historical data.

You can configure S3 Lifecycle rules to transition the exported data to lower-cost storage (e.g., S3 Glacier or S3 Standard-IA) and eventually delete it after 5 years.

No need for additional ETL tools like Glue or DataBrew unless complex transformations are required.

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.

To access an EFS file system across accounts and VPCs, the EFS must be mounted using VPC peering or AWS Transit Gateway, and the EC2 instances must use the amazon-efs-utils package with the correct mount target or access point.

Using an EFS access point simplifies access management, especially across accounts, by providing a POSIX identity and access policy layer.

VPC sharing doesn’t support EFS directly unless the subnet and resources are shared properly, which requires redeployment. Therefore, option D is the most complete and correct.

AWS Shield Advanced provides advanced DDoS protection for AWS workloads, including EC2, CloudFront, and RDS. When integrated with CloudFront, Shield Advanced offers comprehensive detection and mitigation against large and sophisticated DDoS attacks, along with 24x7 access to the AWS DDoS Response Team (DRT). AWS WAF provides application-level protection, but for complete DDoS mitigation, Shield Advanced is the recommended solution.

Reference Extract from AWS Documentation / Study Guide:

"AWS Shield Advanced provides expanded DDoS attack protection for applications running on AWS. It offers always-on detection and automatic inline mitigations that minimize application downtime and latency."

Source: AWS Certified Solutions Architect – Official Study Guide, Security and DDoS Protection section.

Given the large size (180 TB) of the database and the time constraint,AWS Snowball Edge Storage Optimizedis the best solution. Snowball Edge allows for the physical transfer of large datasets to AWS efficiently without relying on slow internet connections.AWS DMSandSCTcan be used to perform ongoing replication of any changes made during the migration, ensuring minimal downtime.

Option B (VPN): Using a 100 Mbps internet connection would take far too long to transfer 180 TB.

Option C (Direct Connect): Establishing a 10 Gbps Direct Connect link might not be feasible within the 2-week timeframe.

Option D (DataSync over internet): With the existing internet connection, DataSync would also take too long.

AWS References:

AWS Snowball Edge

AWS DMS

Amazon FSx for NetApp ONTAP fully supports native NetApp SnapMirror replication, making it the most efficient and reliable option for migrating NetApp data from on-premises to AWS.

From AWS Documentation:

“You can use SnapMirror to replicate data from your on-premises NetApp systems to FSx for ONTAP for seamless, block-level, incremental transfers. This provides a highly efficient and performant method for migration, especially for large datasets.”

(Source: Amazon FSx for NetApp ONTAP – Migration Guide)

Why Option C is correct:

SnapMirror offers block-level replication, making it highly efficient for millions of small files.

It supports NFS and SMB file shares, preserving directory structures and permissions.

Reduces cutover time and allows for incremental syncs.

Uses the existing 10 Gbps network for fast transfers.

Why the other options are incorrect:

Option A (DataSync): Suitable for many file-based migrations but less efficient for very large datasets with millions of small files compared to SnapMirror.

Option B (Storage Gateway): Volume Gateway is not used for full-scale file migrations; it's for hybrid cloud access.

Option D (Snowball Edge): Useful for offline migrations, but online SnapMirror is more efficient and avoids shipping delays.

The most cost-effective and low-maintenance solution to aggregate S3 data from multiple accounts within the same AWS Region is to use Amazon S3 Same-Region Replication (SRR).

From AWS S3 Documentation:

"S3 Same-Region Replication (SRR) automatically replicates new objects between buckets in the same AWS Region. SRR is commonly used to aggregate logs into a single bucket, simplify data aggregation, and meet compliance requirements."

(Source: Amazon S3 User Guide – Replication Overview)

Key benefits of using SRR for this use case:

Zero operational overhead – No Lambda, scripts, or custom code

Fully managed – AWS handles all replication automatically

Secure and efficient – No data leaves the us-west-2 Region

Supports cross-account replication – With proper IAM roles and permissions

Low-cost – Compared to custom ETL pipelines or Lambda solutions

In contrast:

Option A (Lifecycle policies) do not support copying, only expiration, transitions, or deletions.

Option C (scripts) requires custom scheduling and maintenance, increasing operational overhead.

Option D (Lambda) adds complexity and cost and is not as scalable or hands-off as SRR.

To grant the ECS application the least privilege, you create an IAM policy that explicitly lists the ARNs of the specific S3 buckets the task should access. You then attach this policy to the task role (not the instance role), ensuring only that task has the necessary permissions. This approach avoids granting permissions to other EC2 instances or services and restricts access strictly to the required buckets.

AWS Documentation Extract:

"Attach an IAM policy granting access to specific resources to the ECS task role, not to the instance role, to ensure that only the container has access according to the principle of least privilege."

"Use explicit ARNs to control access only to specified S3 buckets."

(Source: Amazon ECS documentation, IAM Roles for Tasks)

A: Tag-based access control for S3 buckets is possible but less direct and commonly used for identity-based access.

C: Attaching the policy to the instance role could grant unintended permissions to all containers or services running on the instance.

D: Wildcard ARNs grant broader access than necessary.

AWS Lambda for Operational Efficiency:

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

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

Amazon DynamoDB:

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

Amazon SNS for Email Notifications:

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

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

Event Scheduling:

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

Why Not EC2 or SQS?:

Option A & Bsuggest running a cron job on anAmazon EC2instance. This approach requires you to manage, scale, and patch the EC2 instance, which increases operational overhead. Lambda is a better choice because it automatically scales and doesn’t require server management.

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

AWS References:

AWS Lambda

Amazon SNS

Amazon DynamoDB

Amazon EventBridge

Summary:

UsingAWS Lambdacombined withAmazon SNSto send notifications, and scheduling the function withAmazon EventBridgeto run daily, is the most operationally efficient solution. It leverages AWS serverless technologies, which reduce the need for infrastructure management and provide automatic scaling. Therefore,Option Cis the correct and optimal choice.

DynamoDB Accelerator (DAX) is a fully managed, in-memory cache for DynamoDB that delivers up to a 10x performance improvement—even at millions of requests per second. DAX requires minimal changes to existing applications and offloads the read workload from DynamoDB during report generation.

Reference Extract:

"DAX provides in-memory acceleration for DynamoDB tables, reducing response times from milliseconds to microseconds with minimal application changes."

Source: AWS Certified Solutions Architect – Official Study Guide, DynamoDB and Performance section.

AWS recommends using AWS IAM Identity Center (formerly AWS SSO) for centralized authentication and access control across multiple accounts in an AWS Organization, especially when integrating with an external IdP.

From AWS Documentation:

“Use IAM Identity Center to provide centralized access to multiple AWS accounts or applications. You can integrate with an external IdP via SAML 2.0. Assign users permissions through permission sets that define the roles users can assume.”

(Source: AWS IAM Identity Center User Guide)

Why B and E are correct:

E enables centralized identity federation using IAM Identity Center with your external IdP.

B uses permission sets to apply least-privilege access roles to users and groups across accounts, in alignment with the principle of least privilege.

Why others are incorrect:

Option A: IAM users in each account break centralized access model and are hard to manage at scale.

Option C: Managing individual IAM roles and inline policies across accounts is not scalable.

Option D: Per-account SAML providers are redundant when using IAM Identity Center, which provides centralized federation.

This architecture uses ALB for routing, Auto Scaling for elasticity, SQS to buffer unprocessed orders and decouple services, and RDS Multi-AZ for high availability and durability of transactional data. This ensures resilience and fault tolerance even during traffic spikes.

Amazon Rekognition provides pretrained models that can detect inappropriate or unsafe content (such as nudity or violence) without requiring users to build their own ML models. Using a Lambda function to call Rekognition when new photos are uploaded is a serverless and scalable solution.

When an application is fronted by an Application Load Balancer (ALB) and malicious traffic is detected from a specific IP, the correct way to block the IP is by using AWS WAF (Web Application Firewall).

With AWS WAF, you can create an IP Set to include the offending IP address or range.

Then create a Web ACL (Access Control List) with a rule set to BLOCK requests from that IP set.

Finally, associate the Web ACL with the ALB.

Security groups (Option A) cannot deny specific IPs because they are stateful and allow-only rules.

Amazon Detective (Option B) is a security analysis and investigation tool; it doesn’t block traffic.

AWS RAM (Option C) is for resource sharing across accounts, not for blocking IPs.

This approach aligns with AWS’s Security Pillar of the Well-Architected Framework and is fully managed, with minimal operational effort.

???? Reference:

Using AWS WAF with an Application Load Balancer

Block IPs with AWS WAF

Key Requirements:

Serverless architecture.

Handle traffic bursts with high availability.

Process orders asynchronouslyin the order they are received.

Analysis of Options:

Option A:Amazon SNS delivers messages to subscribers. However, SNS does not ensure ordering, making it unsuitable for FIFO (First In, First Out) requirements.

Option B:Amazon SQS FIFO queues support ordering and ensure messages are delivered exactly once. AWS Lambda functions can be triggered by SQS to process messages asynchronously and efficiently. This satisfies all requirements.

Option C:Amazon SQS standard queues do not guarantee message order and have "at-least-once" delivery, making them unsuitable for the FIFO requirement.

Option D:Similar to Option A, SNS does not ensure message ordering, and using AWS Batch adds complexity without directly addressing the requirements.

AWS References:

Amazon SQS FIFO Queues

AWS Lambda and SQS Integration

Option Auses an IAM role attached to the EC2 instance profile, enabling secure and automated access to Secrets Manager. This is the recommended approach.

Option Buses IAM users, which is less secure and harder to manage.

Option Cis not practical for accessing secrets programmatically.

Option Dviolates best practices by granting direct access to the EC2 instance.

AWS Config is a service designed to assess, audit, and evaluate the configurations of AWS resources. It continuously monitors and records your AWS resource configurations and allows you to automate the evaluation of recorded configurations against desired configurations. By starting a configuration recorder, AWS Config will capture changes to supported resource types as configuration items—without the need to modify any of the existing resources. This provides a full history of configuration changes and is specifically intended for exactly this use case.

AWS Documentation Extract:

“AWS Config provides a detailed view of the configuration of AWS resources in your AWS account. This includes how the resources are related to one another and how they were configured in the past so you can see how the configurations and relationships change over time.”

“You can start the configuration recorder, which will record the configuration changes of the supported resources in your AWS account.”

(Source: AWS Config documentation, What is AWS Config?)

Other options:

B: CloudFormation drift detection only works for resources created and managed by CloudFormation and requires stacks.

C: Amazon Detective is used for analyzing and investigating security findings, not for resource configuration tracking.

D: AWS Audit Manager is used for automating evidence collection to help with audits, not for tracking resource configurations.

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

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

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

AWSService Catalogis designed to allow organizations to manage a catalog of approved products (including AMIs) that users can deploy. By creating a portfolio that contains only EC2 instances launched with preapproved AMIs, the company can enforce compliance with the approved operating system and software for all EC2 instances. Service Catalog also streamlines the process of launching EC2 instances, reducing the lead time while ensuring that developers use only the approved configurations.

Option B (EC2 Image Builder): While EC2 Image Builder helps in creating and managing AMIs, it doesn't provide the enforcement mechanism that Service Catalog does.

Option C (EventBridge rule and Systems Manager script): This solution is reactive and involves more operational complexity compared to Service Catalog.

Option D (AWS Config rule): This option is reactive (it terminates non-compliant instances after launch) and introduces additional operational overhead.

AWS References:

AWS Service Catalog

Amazon API Gateway supports multiple API types: REST, HTTP, WebSocket, and gRPC. HTTP APIs are a newer, lightweight option that support JWT authorizers natively, enabling secure, scalable authentication for APIs with JSON Web Tokens.

HTTP APIs can integrate with ALBs as a backend target, providing direct connectivity and simplified API management with JWT authentication built in.

REST APIs support JWT but are more feature-rich and complex, often used for legacy or more complex use cases, and have higher costs and latency. WebSocket APIs are for real-time, bidirectional communication, which is not requested here. gRPC APIs support RPC calls but are less common for public HTTP-based APIs with JWT auth.

Therefore, HTTP API with JWT authorizers is the best fit for this use case.

Explanation (AWS Docs):

DynamoDB Accelerator (DAX) is a fully managed, in-memory cache specifically for DynamoDB. It reduces read load and latency without requiring code changes (only SDK config). This is the least development effort.

“Amazon DynamoDB Accelerator (DAX) is a fully managed, highly available in-memory cache for DynamoDB that delivers microsecond read performance and requires minimal application changes.”

— Amazon DAX

Option A: Importing customer-managed keys into AWS KMS ensures that encryption key material remains under the company’s control.

Option D: S3 Glacier with server-side encryption using customer-provided keys (SSE-C) complies with the need for controlled encryption and provides cost-effective storage for backups.

AWS Key Management Service Importing Keys Documentation,S3 Encryption Documentation

The requirement is to monitor network metrics such as total packet loss and round-trip time (RTT) between on-premises and AWS over Site-to-Site VPN with minimal operational overhead. AWS CloudWatch Network Monitor (formerly known as VPC Network Manager) provides a managed solution to monitor connectivity, including packet loss and latency, between AWS and on-premises networks. This solution does not require managing any additional infrastructure like EC2 instances or Lambda functions and thus reduces operational overhead significantly.

CloudWatch Network Monitor leverages AWS-managed probes and integrates natively with CloudWatch dashboards and alarms, enabling automated, centralized monitoring of network health. This aligns with the AWS Well-Architected Framework’s operational excellence pillar by minimizing manual intervention and enabling proactive detection of network issues.

Option B, C, and D involve creating custom probes with EC2, Lambda, or Batch jobs, which increases complexity, cost, and maintenance effort. They also require scheduling, script management, and additional monitoring infrastructure.

Amazon RDS supports encryption at rest by using AWS KMS keys backed by AWS CloudHSM. This allows use of dedicated FIPS 140-2 Level 3 validated hardware modules to manage encryption keys, meeting compliance for sensitive data such as PII.

From AWS Documentation:

“You can use AWS KMS with keys that are backed by AWS CloudHSM to control the encryption of RDS databases. This provides dedicated HSM-backed key storage and management.”

(Source: Amazon RDS User Guide – Encrypting Amazon RDS Resources)

Why B is correct:

Meets the requirement for dedicated HSM hardware.

Fully integrates with RDS for transparent encryption at rest.

Satisfies compliance standards for healthcare and regulated data.

Why others are incorrect:

A: Keys in CloudHSM directly are not used by RDS; they must be managed through KMS integration.

C: EC2 instance stores are ephemeral, not suitable for RDS databases.

D: SSE-S3 applies to S3 objects, not databases.

To enable DNS resolution from AWS VPCs to on-premises DNS servers over Direct Connect or VPN, AWS recommends using Amazon Route 53 Resolver with outbound endpoints. An outbound endpoint allows DNS queries originating in the VPC to be forwarded to a customer-managed DNS server (e.g., on-prem).

Next, a forwarding rule (Forward type) must be created to forward DNS queries for the custom domain internal.example.com to the on-premises DNS IP addresses. This rule defines what domain names are forwarded and to which DNS servers.

Finally, the rule must be associated with all workload VPCs to allow those VPCs to use the rule. There is no need to deploy endpoints in every VPC — one outbound endpoint is sufficient and can be shared across VPCs via rule association.

???? Reference:

Route 53 Resolver Endpoints

Best practices for hybrid DNS resolution

AWS Lambda supports encrypting environment variables at rest using AWS KMS. You can use encryption helpers (or Lambda’s built-in support) to encrypt sensitive environment variable values using a KMS key. These encrypted variables are not visible in plaintext to developers, either in the console or when running the code.

AWS Documentation Extract:

"AWS Lambda automatically encrypts environment variables at rest. For additional security, you can use AWS KMS keys and encryption helpers to encrypt environment variables, ensuring they are never exposed in plaintext."

(Source: AWS Lambda documentation, Environment Variables Security)

A: Does not address the issue (and adds more management overhead).

B, C: There is no native support for environment variable encryption via CloudHSM or ACM.

AWS Global Accelerator reduces latency by directing users to the optimal Regional endpoint based on global network health and proximity. Amazon CloudFront caches static and dynamic content at edge locations for ultra-low latency access worldwide, improving performance and reducing server load.

To meet the requirements for tagging and preventing instance creation or deletion without proper tags, the company can use a combination of AWS Organizations tag policies and service control policies (SCPs).

Tag Policies: These enforce specific tag values across resources. Creating a tag policy with required values (e.g., sensitive, non-sensitive) and attaching it to the appropriate organizational unit (OU) ensures consistency in tagging.

SCPs: SCPs can be used to enforce compliance by preventing instance creation without a tag and preventing tag deletion. These policies control actions at the account level across the organization.

Key AWS features:

Tag Policieshelp standardize tags across accounts, andSCPsenforce governance by restricting actions that violate the policies.

AWS Documentation: AWS best practices recommend using tag policies and SCPs to enforce compliance across multiple accounts within AWS Organizations?.

The requirement is to route users to the nearest AWS Region where the application is deployed. The best solution is to use Amazon Route 53 with a geolocation routing policy, which routes traffic based on the geographic location of the user making the request.

Geolocation Routing: This routing policy ensures that users are directed to the resources (in this case, EC2 instances) that are geographically closest to them, thereby reducing latency and improving the user experience.

Application Load Balancer (ALB): Within each Region, an internet-facing Application Load Balancer (ALB) is used to distribute incoming traffic across multiple EC2 instances in different Availability Zones. ALBs are designed to handle HTTP/HTTPS traffic and provide advanced features like content-based routing, SSL termination, and user authentication.

Why Not Other Options?:

Option B (Geoproximity + NLB): Geoproximity routing is similar but more complex as it requires fine-tuning the proximity settings. A Network Load Balancer (NLB) is better suited for TCP/UDP traffic rather than HTTP/HTTPS.

Option C (Multivalue Answer Routing + ALB): Multivalue answer routing does not direct traffic based on user location but rather returns multiple values and lets the client choose. This does not meet the requirement for geographically routing users.

Option D (Weighted Routing + NLB): Weighted routing splits traffic based on predefined weights and does not consider the user's geographic location. NLB is not ideal for this scenario due to its focus on lower-level protocols.

AWS References:

Amazon Route 53 Routing Policies- Detailed explanation of the various routing policies available in Route 53, including geolocation.

Elastic Load Balancing- Information on the different types of load balancers in AWS and when to use them.

Express Workflows in AWS Step Functions are optimized for high-throughput, short-duration, and low-cost workflows. They are suitable for applications with large volumes of parallel and interdependent tasks. When paired with HTTP APIs from API Gateway, which are more cost-efficient than REST APIs, this setup offers scalability and cost-effectiveness.

Comprehensive and Detailed Explanation:

To securely migrate an on-premises Oracle database to Amazon Aurora, the following steps are recommended:

Use AWS Schema Conversion Tool (AWS SCT) and AWS Database Migration Service (AWS DMS): AWS SCT helps convert the source database schema to a format compatible with the target database (Aurora). AWS DMS facilitates the actual data migration, ensuring minimal downtime and data integrity.

Use AWS Site-to-Site VPN: Establishing a Site-to-Site VPN connection provides a secure and encrypted tunnel between the on-premises environment and AWS. This ensures that data transferred during the migration is protected against interception and unauthorized access.

Comprehensive and Detailed Explanation:

To prevent loss of access to the AWS root user account due to a lost MFA device, AWS recommends enabling multiple MFA devices for the root user.

Multiple MFA Devices: AWS allows up to eight MFA devices (virtual, hardware, or security keys) to be associated with a single root user account. This redundancy ensures that if one device is lost, others can be used to authenticate and access the account.

Security Best Practices: Implementing multiple MFA devices enhances account security and provides resilience against potential access issues.

By proactively setting up multiple MFA devices, the company can maintain uninterrupted access to its critical AWS resources, even in the event of a lost or malfunctioning MFA device.

Versioning in S3 preserves every version of every object — preventing permanent overwrites. This ensures compliance where data cannot be overwritten or lost.

SSE-S3 ensures server-side encryption.

“When you enable versioning, Amazon S3 stores every version of every object. With versioning, you can preserve, retrieve, and restore every version of every object stored in an S3 bucket.”

— S3 Versioning

This satisfies regulatory requirements for protecting data from overwriting indefinitely.

Incorrect Options:

B: Object Lock with retention period is time-bound, not indefinite.

C: Legal hold blocks deletion but doesn’t directly prevent overwriting.

D: Storage Lens is analytics, not protection.

Amazon S3 is suitable for storing data that needs to be accessed weekly and integrates with AWS Key Management Service (KMS) to provide encryption at rest with server-side encryption using KMS-managed keys (SSE-KMS).

SSE-KMS uses envelope encryption and allows automatic key rotation and logging through AWS CloudTrail, satisfying the requirements for audit trails and compliance.

S3 Glacier Deep Archive is unsuitable due to its high retrieval latency. SSE-C requires customer-side management of encryption keys, with no support for automatic rotation or audit. SSE-S3 does not use customer-managed keys and lacks fine-grained control and auditing.

Lambda supports mounting an Amazon EFS file system inside your function to store larger dependencies beyond the 250 MB layer quota.

EFS automatically encrypts data in transit using TLS.

“You can configure your Lambda function to mount an Amazon EFS file system, enabling your function to access large amounts of data or large dependencies.”

“Amazon EFS automatically encrypts all data at rest and in transit.”

— Lambda with Amazon EFS

This is the least operational overhead approach.

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

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

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

AWS documentation on this process:

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

AWS CloudFront Documentation

Why the other options are incorrect:

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

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

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

The combination of these solutions provides an efficient and automated way to migrate data while preserving metadata and ensuring cleanup:

Create a new S3 bucket with versioning enabled(Option A) to preserve object metadata like version IDs during migration.

UseS3 batch operations(Option B) to efficiently copy data from specific prefixes in the source bucket to the destination bucket, ensuring minimal overhead.

Use an S3 Lifecycle policy(Option F) to automatically delete the data from the source bucket after it has been migrated, reducing manual intervention.

Option C (CopyObject API): This approach would require more manual scripting and effort.

Option D (Same-Region Replication): SRR is designed for ongoing replication, not for one-time migrations.

Option E (DataSync): DataSync adds more complexity than necessary for this task.

AWS References:

S3 Batch Operations

S3 Lifecycle Policies