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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.
Option C is correct because SageMaker Model Monitor is the purpose-built service for continuous monitoring of a deployed endpoint: it captures inference data (data capture) and runs monitoring schedules that compare incoming data against a baseline to detect data drift and other violations. Option B is correct because Model Monitor emits CloudWatch metrics and events, so an EventBridge rule can match those violation events and automatically trigger a retraining pipeline, closing the loop from detection to remediation. Option A is not appropriate because manual monthly review is not continuous and cannot reliably catch drift in time. Option D is insufficient because CloudWatch Logs Insights only queries logs for anomalies; it does not compute drift against a baseline or trigger retraining. Option E is unrelated because A/B testing compares model variants, not drift detection or retraining.
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
Monthly manual review cannot detect drift as it emerges, and human inspection does not compute statistical divergence between live and training distributions. Manual review suits one-off audits or low-volume models where automated baselines are impractical, not continuous monitoring with automated retraining triggers.
- ✓
Configure an Amazon EventBridge rule to start a retraining pipeline when the Model Monitor detects violations.
Why this is correct
EventBridge rules react to Model Monitor violation events by triggering the retraining pipeline automatically. This closes the loop between drift detection and remediation, satisfying the requirement for continuous monitoring that retrains the model without manual intervention when data drift degrades performance.
- ✓
Enable SageMaker Model Monitor to capture inference data and run monitoring schedules.
Why this is correct
Model Monitor captures endpoint inference data and runs scheduled jobs comparing it against baselines, detecting drift in the live traffic. This provides the continuous visibility the scenario requires, feeding the violation events that drive automated retraining.
- ✗
Use Amazon CloudWatch Logs Insights to query inference logs for anomalies.
Why it's wrong here
CloudWatch Logs Insights queries raw inference logs, which lack baseline statistics and drift metrics; it cannot compute distribution shifts against training data. It is correct for ad-hoc troubleshooting and error investigation, whereas drift detection requires SageMaker Model Monitor with a baseline.
- ✗
Deploy the model on multiple endpoints with A/B testing to compare performance.
Why it's wrong here
A/B testing compares two model variants' live performance; it does not detect input feature drift or trigger retraining. It is the right choice when validating whether a new model version outperforms the incumbent before full rollout, not for continuous drift monitoring of a single deployed model.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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