AWS Certified Machine Learning Engineer – Associate (MLA-C01) — Question 3

A company has trained and deployed an ML model by using Amazon SageMaker. The company needs to implement a solution to record and monitor all the API call events for the SageMaker endpoint. The solution also must provide a notification when the number of API call events breaches a threshold.
Which solution will meet these requirements?

Answer options

• A. Use SageMaker Debugger to track the inferences and to report metrics. Create a custom rule to provide a notification when the threshold is breached.
• B. Use SageMaker Debugger to track the inferences and to report metrics. Use the tensor_variance built-in rule to provide a notification when the threshold is breached.
• C. Log all the endpoint invocation API events by using AWS CloudTrail. Use an Amazon CloudWatch dashboard for monitoring. Set up a CloudWatch alarm to provide notification when the threshold is breached.
• D. Add the Invocations metric to an Amazon CloudWatch dashboard for monitoring. Set up a CloudWatch alarm to provide notification when the threshold is breached.

Correct answer: C

Explanation

The correct answer is C because AWS CloudTrail logs all API calls made to the SageMaker endpoint, allowing for comprehensive monitoring. Additionally, using Amazon CloudWatch to set alarms ensures timely notifications when the predefined threshold is exceeded. Options A and B focus on SageMaker Debugger, which is not designed for tracking API call events, while option D does not provide a method for logging the events themselves.