AWS Certified Machine Learning Engineer – Associate (MLA-C01) — Question 90

A company deployed an ML model that uses the XGBoost algorithm to predict product failures. The model is hosted on an Amazon SageMaker endpoint and is trained on normal operating data. An AWS Lambda function provides the predictions to the company's application.

An ML engineer must implement a solution that uses incoming live data to detect decreased model accuracy over time.

Which solution will meet these requirements?

Answer options

• A. Use Amazon CloudWatch to create a dashboard that monitors real-time inference data and model predictions. Use the dashboard to detect drift.
• B. Modify the Lambda function to calculate model drift by using real-time inference data and model predictions. Program the Lambda function to send alerts.
• C. Schedule a monitoring job in SageMaker Model Monitor. Use the job to detect drift by analyzing the live data against a baseline of the training data statistics and constraints.
• D. Schedule a monitoring job in SageMaker Debugger. Use the job to detect drift by analyzing the live data against a baseline of the training data statistics and constraints.

Correct answer: C

Explanation

The correct answer is C, as SageMaker Model Monitor is specifically designed to monitor model performance and detect drift by comparing live data against established baselines from training data. Option A, while useful for monitoring, does not directly analyze drift; B modifies the Lambda function but lacks the comprehensive analysis capability. Option D refers to SageMaker Debugger, which is not focused on detecting drift but rather on debugging models during training.