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MLA-C01 Practice Question: A company deploys a model on SageMaker that…
A company deploys a model on SageMaker that serves predictions to a web application. The model's performance degrades over time due to data drift. The company wants to set up continuous monitoring. Which TWO actions should the company take to monitor and retrain the model effectively? (Choose TWO.)
⚠ Common exam trap
A common mix-up: candidates confuse general monitoring tools like CloudWatch Logs Insights with the specialized, model-aware monitoring capabilities of SageMaker Model Monitor, or they may overlook that EventBridge automation requires Model Monitor to be enabled first.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Configure an Amazon EventBridge rule to start a retraining pipeline when the Model Monitor detects violations.
Amazon EventBridge can be configured to trigger a retraining pipeline automatically when SageMaker Model Monitor detects data drift or other violations, enabling a closed-loop monitoring and retraining system. Option C is correct because SageMaker Model Monitor must first be enabled to capture inference data and run monitoring schedules, which is the prerequisite for detecting drift and triggering automated actions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Manually review model performance monthly and retrain if necessary.
Why it's wrong here
Manual review is not automated and may miss drift between reviews.
- ✓
Configure an Amazon EventBridge rule to start a retraining pipeline when the Model Monitor detects violations.
Why this is correct
EventBridge can react to Model Monitor violation events to trigger automatic retraining.
- ✓
Enable SageMaker Model Monitor to capture inference data and run monitoring schedules.
Why this is correct
Model Monitor captures data and runs statistics to detect drift.
- ✗
Use Amazon CloudWatch Logs Insights to query inference logs for anomalies.
Why it's wrong here
CloudWatch Logs Insights can analyze logs but not automatically detect drift or trigger retraining.
- ✗
Deploy the model on multiple endpoints with A/B testing to compare performance.
Why it's wrong here
A/B testing compares variants but does not monitor drift automatically.
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