AWS Certified Machine Learning Engineer – Associate (MLA-C01) — Question 193

A company is using an Amazon SageMaker AI ML model to predict traffic accidents that potholes cause. An ML engineer has configured SageMaker Model Monitor to run as part of a SageMaker AI pipeline. In the MonitoringExecution output, the ML engineer observes several baseline_drift_check violations that are failing the pipeline.

What should the ML engineer do to resolve this issue?

Answer options

• A. Retrain the model by using a new SageMaker AI training job. Check for errors by using SageMaker Debugger.
• B. Retrain the model with new training data. Reuse the original baseline in Model Monitor.
• C. Retrain the model with new training data. Use the new baseline in Model Monitor.
• D. Rerun the SageMaker AI pipeline after enabling the emit_metrics option in the baseline constraints file.

Correct answer: C

Explanation

The correct action is to retrain the model with new training data and use the new baseline in Model Monitor (option C), as this allows the model to adapt to changes in data patterns. Options A and B do not incorporate a new baseline, which is necessary to address the baseline drift. Option D does not resolve the underlying issue of drift violations.