Comprehensive and Detailed Step-by-Step Explanation:
To meet the requirements of copying only relevant data as soon as possible and receiving notifications upon completion, the following steps are recommended:
* Generate a Manifest File (Option A):
* Action:Modify the on-premises data generation process to create a manifest file at the end of each data generation cycle. This manifest should list the names of the objects that need to be copied to Amazon S3.
* Implementation:Develop a custom script that runs after data generation. This script compiles the list of relevant data files into a manifest file and uploads it to a designated S3 bucket.
* Justification:Using a manifest allows AWS DataSync to transfer only the specified files, reducing unnecessary data transfer and associated costs.
docs.aws.amazon.com
* Automate DataSync Task Execution (Option D):
* Action:Set up an S3 Event Notification to trigger an AWS Lambda function whenever a new manifest file is uploaded to the S3 bucket.
* Implementation:Configure the Lambda function to invoke the DataSync task by calling the StartTaskExecution API action, specifying the manifest file. This ensures that only the files listed in the manifest are copied from on-premises storage to Amazon S3.
* Justification:This automation ensures timely data transfer as soon as relevant data is available, minimizing delays and manual intervention.
docs.aws.amazon.com
* Set Up Completion Notifications (Option E):
* Action:Create an Amazon SNS topic to handle notifications. Then, establish an Amazon EventBridge rule that monitors the DataSync task execution status and sends an email notification to the SNS topic when the status changes to SUCCESS or ERROR.
* Implementation:Configure EventBridge to capture state changes of the DataSync task. When a task completes successfully or encounters an error, EventBridge triggers a notification to the SNS topic, which then sends an email to the subscribed recipients.
* Justification:This setup provides immediate feedback on the data transfer process, allowing the analytics team to act promptly based on the success or failure of the data copy operation.
docs.aws.amazon.com
References:
AWS DataSync User Guide:Transferring specific files or objects by using a manifest AWS DataSync API Reference:StartTaskExecution Amazon EventBridge User Guide:Creating an EventBridge rule that triggers on an AWS API call Amazon SNS User Guide:Sending Amazon SNS messages to HTTP/HTTPS endpoints

Amazon RDS Proxyis the correct answer because it is specifically designed to improve connection handling for relational databases, including Aurora. AWS documentation explains that RDS Proxy providesconnection pooling, helps handleunpredictable surges in database traffic, and automatically determines the current Aurora writer instance. That reduces the stress of large numbers of short-lived connections and improves application behavior during failover events because clients reconnect through the proxy rather than directly to database endpoints. Switching database engines would be unnecessary and disruptive. DAX is for DynamoDB, not Aurora. Therefore, placingRDS Proxyin front of the Aurora cluster is the most direct way to reduce connection pressure and improve failover behavior. (AWS Documentation)

Amazon EMR instance fleets are the correct design because they provide the most flexible and operationally efficient way to launch clusters across different EC2 instance types, Availability Zones, and purchasing options. In this scenario, the company wants On-Demand Instances for core nodes, Spot Instances for task nodes, and memory optimized instances only where capacity is available.
Instance fleets allow EMR to evaluate multiple subnets in multiple Availability Zones and provision capacity where the requested instance types and purchase models are available. Instance groups are more rigid because they are tied to a more fixed instance configuration. A single-subnet design also fails the Availability Zone flexibility requirement. Therefore, using EMR instance fleets across multiple subnets is the best answer.

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.
Reference: AWS Certified Solutions Architect - Official Study Guide, S3 Storage Classes Section.

AWS Fargate with Amazon ECS is a serverless, fully managed compute engine for containers. Fargate eliminates the need to provision or manage servers, and is ideal for periodic, long-running batch jobs. You only pay for the resources consumed during job execution, making it cost-effective for jobs that run infrequently. Lambda is not suitable because the maximum execution time is 15 minutes, but the job can take up to 2 hours.
AWS Documentation Extract:
"AWS Fargate lets you run containers without managing servers or clusters. Fargate is ideal for batch jobs, event-driven processing, and scheduled tasks that require hours of compute." (Source: AWS Fargate documentation) A: Reserved Instances are cost-inefficient for infrequent workloads and require server management.
B: Lambda has a hard limit of 15 minutes per execution, insufficient for this job.
D: ECS on EC2 requires you to manage and provision EC2 instances, increasing cost and management overhead.
Reference: AWS Certified Solutions Architect - Official Study Guide, Serverless and Batch Processing.