AWS Certified Machine Learning – Specialty — Question 177

A machine learning (ML) specialist is administering a production Amazon SageMaker endpoint with model monitoring configured. Amazon SageMaker Model
Monitor detects violations on the SageMaker endpoint, so the ML specialist retrains the model with the latest dataset. This dataset is statistically representative of the current production traffic. The ML specialist notices that even after deploying the new SageMaker model and running the first monitoring job, the SageMaker endpoint still has violations.
What should the ML specialist do to resolve the violations?

Answer options

• A. Manually trigger the monitoring job to re-evaluate the SageMaker endpoint traffic sample.
• B. Run the Model Monitor baseline job again on the new training set. Configure Model Monitor to use the new baseline.
• C. Delete the endpoint and recreate it with the original configuration.
• D. Retrain the model again by using a combination of the original training set and the new training set.

Correct answer: B

Explanation

The correct answer is B because running the Model Monitor baseline job again with the new training set ensures that the model monitoring is aligned with the most recent data, allowing for accurate detection of violations. The other options do not effectively address the need to update the baseline for monitoring, which is essential for resolving the ongoing violations.