AWS Certified Machine Learning Engineer – Associate (MLA-C01) — Question 11

Case Study -
A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring.
The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.
The company needs to run an on-demand workflow to monitor bias drift for models that are deployed to real-time endpoints from the application.
Which action will meet this requirement?

Answer options

• A. Configure the application to invoke an AWS Lambda function that runs a SageMaker Clarify job.
• B. Invoke an AWS Lambda function to pull the sagemaker-model-monitor-analyzer built-in SageMaker image.
• C. Use AWS Glue Data Quality to monitor bias.
• D. Use SageMaker notebooks to compare the bias.

Correct answer: A

Explanation

The correct answer is A because invoking an AWS Lambda function to run a SageMaker Clarify job directly addresses the requirement for monitoring bias drift in the deployed models. The other options do not specifically provide a mechanism for bias monitoring in real-time endpoints or are not designed for this purpose, making them unsuitable for the requirement.