AWS Certified Machine Learning Engineer – Associate (MLA-C01) — Question 62

A company has used Amazon SageMaker to deploy a predictive ML model in production. The company is using SageMaker Model Monitor on the model. After a model update, an ML engineer notices data quality issues in the Model Monitor checks.
What should the ML engineer do to mitigate the data quality issues that Model Monitor has identified?

Answer options

• A. Adjust the model's parameters and hyperparameters.
• B. Initiate a manual Model Monitor job that uses the most recent production data.
• C. Create a new baseline from the latest dataset. Update Model Monitor to use the new baseline for evaluations.
• D. Include additional data in the existing training set for the model. Retrain and redeploy the model.

Correct answer: C

Explanation

The correct answer is C because creating a new baseline from the latest dataset ensures that the Model Monitor can accurately reflect the current data distribution and quality. Options A and D do not address the data quality issues directly, while B only runs a monitor job without updating the baseline, which is necessary for effective monitoring.