AWS Certified Machine Learning Engineer – Associate (MLA-C01) — Question 147

A company has an ML model in Amazon SageMaker AI. An ML engineer needs to implement a monitoring solution to automatically detect changes in the input data distribution of model features.

Which solution will meet this requirement with the LEAST operational overhead?

Answer options

• A. Configure SageMaker Model Monitor. Establish a data quality baseline. Ensure that the emit_metrics option is enabled in the baseline constraints file. Configure an Amazon CloudWatch alarm to notify the company about changes in specific metrics that are related to data quality.
• B. Configure SageMaker Model Monitor. Establish a model quality baseline. Ensure that the comparison_method option is set to Robust in the baseline constraints file. Configure an Amazon CloudWatch alarm to notify the company about changes in model quality metrics.
• C. Use SageMaker Debugger with custom rules to track shifts in feature distributions. Configure Amazon CloudWatch alarms to notify the company when the rules detect significant changes.
• D. Use Amazon CloudWatch to directly observe the SageMaker AI endpoint’s performance metrics. Manually analyze the CloudWatch logs for indicators of data drift or shifts in feature distribution.

Correct answer: A

Explanation

The correct option is A because SageMaker Model Monitor is specifically designed to monitor data quality and can automatically alert about changes in input data distribution with minimal manual intervention. Options B and C focus on model quality and custom rules respectively, which may require more operational overhead. Option D involves a manual analysis process, which is less efficient than automated monitoring.