AWS Certified Machine Learning Engineer – Associate (MLA-C01) — Question 78

A company wants to reduce the cost of its containerized ML applications. The applications use ML models that run on Amazon EC2 instances, AWS Lambda functions, and an Amazon Elastic Container Service (Amazon ECS) cluster. The EC2 workloads and ECS workloads use Amazon Elastic Block Store (Amazon EBS) volumes to save predictions and artifacts.
An ML engineer must identify resources that are being used inefficiently. The ML engineer also must generate recommendations to reduce the cost of these resources.
Which solution will meet these requirements with the LEAST development effort?

Answer options

• A. Create code to evaluate each instance's memory and compute usage.
• B. Add cost allocation tags to the resources. Activate the tags in AWS Billing and Cost Management.
• C. Check AWS CloudTrail event history for the creation of the resources.
• D. Run AWS Compute Optimizer.

Correct answer: D

Explanation

The correct answer is D because AWS Compute Optimizer provides automated recommendations to optimize resource usage and reduce costs with minimal development effort. Option A requires custom code development, which increases effort, while option B, while useful for tracking costs, does not directly identify inefficiencies. Option C involves manual checking of logs, which is not efficient for this task.