MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

• ✗

  Use SageMaker Clarify to detect data drift on the endpoint

  Why it's wrong here

  Clarify explains model predictions and detects bias; it does not schedule endpoint data-quality monitoring or compute drift statistics against a baseline. It is tempting because Clarify does offer bias and explainability monitoring, which would be correct for analysing feature attribution rather than data drift.

• ✓

  Enable data capture on the endpoint, generate a baseline from training data, create a data quality monitoring schedule, and set up a CloudWatch Alarm on violations

  Why this is correct

  Data quality monitoring requires capturing endpoint requests and responses, generating a baseline statistics file from the training dataset, scheduling the monitor against that baseline, and alarming on violations. Together these detect drift in real-time inference data and notify the team via CloudWatch.

• ✗

  Create a model quality monitoring schedule directly on the endpoint without any baseline

  Why it's wrong here

  A model quality schedule compares predictions with ground-truth labels, and without a baseline no drift statistics can be computed. It is tempting because scheduling directly on the endpoint is a real Model Monitor feature, and it would be correct for monitoring accuracy once labelled data is available.

• ✗

  Enable data capture and rely on SageMaker Model Monitor to automatically infer drift without a baseline

  Why it's wrong here

  Model Monitor requires a baseline dataset and statistics to compute drift; it cannot infer drift automatically from captured data alone. It is tempting because enabling data capture is a genuine prerequisite step, and it would be correct if a baseline had already been generated and attached to the schedule.

Go deeper

Related to this question