Creating a listener rule on the Application Load Balancer (ALB) to return a maintenance response during the maintenance window is the most straightforward solution with the least operational overhead. The rule can be configured to match all incoming requests and return a custom response, and it can be easily removed once maintenance is complete.
* Option A (Aurora table flag): This adds unnecessary complexity for a temporary maintenance response.
* Option B and D (SQS or SNS): These options introduce more components than needed for a simple maintenance message.
AWS References:
* ALB Listener Rules

Amazon FSx for Lustre is a high-performance parallel file system designed for machine learning and HPC that can be linked to Amazon S3 via Data Repository Associations (DRA). With DRA, you can import S3 objects as files (lazy or preloaded) and export results back to S3, providing very high throughput and low- latency POSIX access on the training cluster. This S3-native integration minimizes data loading overhead and accelerates training cycles at scale. EFS (A) is general-purpose and lower throughput per TB than Lustre. EBS (C) requires manual copy and does not scale as a shared parallel filesystem. DataSync alone (D) is a transfer tool, not a high-throughput training filesystem. FSx for Lustre with S3 DRA best satisfies scalability, throughput, and native S3 integration for rapid ML training.

* The workload is bursting and must be able to buffer more orders than the printers can process. The best practice for this is a durable, scalable queue: Amazon SQS.
* SQS decouples the frontend from the backend and can handle large, spiky volumes of messages.
* AWS Lambda is a good fit for processing messages from SQS because it scales automatically with queue depth and requires no server management.
* Deploying the frontend in Docker on Amazon ECS with Fargate provides a fully managed, auto-scaling container environment without managing EC2 instances.
Why others are not correct:
* A and D: SNS is a pub/sub service, not a queue; it does not provide durable back-pressure buffering when the printers are slower than incoming requests. Lambda@Edge is for CloudFront edge processing, not back-end order processing.
* B: Same SNS issue-no durable queue semantics and not ideal for workload that must be buffered and retried.

Option Aintroduces complexity and does not scale well.
Option Bcreates a read replica, offloading read traffic from the primary RDS instance without impacting the critical application.
Option Cis manual and inefficient.
Option Dmight help for caching frequently queried data but is not ideal for ad-hoc reporting.Therefore, Option B is the best choice.

AWS Backup Audit Manager automates auditing and reporting of backup activity and compliance, while AWS Config provides visibility into configuration changes. Together, they provide the simplest, most automated, and compliant backup monitoring solution.
From AWS Documentation:
"AWS Backup Audit Manager automatically audits backup activity across AWS resources. You can use predefined or custom frameworks to monitor backup compliance and encryption status." (Source: AWS Backup Audit Manager User Guide) Why D is correct:
* Ensures centralized visibility into all backup jobs.
* Verifies encryption status automatically.
* Generates ready-to-use reports with minimal operational overhead.
* Complies with regulatory requirements for data protection.
Why others are incorrect:
* A & C: Custom Lambda automation increases maintenance effort.
* B: Manual checking is operationally inefficient and error-prone.
References:
AWS Backup Audit Manager User Guide
AWS Config Documentation - "Compliance and Monitoring"
AWS Well-Architected Framework - Operational Excellence Pillar