MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
A retail company uses a SageMaker Model Monitor data quality monitor on a real-time endpoint. The monitor's baseline was generated from a training dataset in which the "promo_code" feature was often null. In production the feature is now populated for nearly every record, and the monitor reports violations even though model accuracy has not degraded. The team wants the monitor to stop flagging this expected change without disabling monitoring entirely. What should they do?
⚠ Common exam trap
The trap here is treating monitor violations as a monitoring-configuration bug to be silenced, rather than as a stale-baseline problem to be corrected with fresh representative data.
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
✓
Regenerate the baseline statistics and constraints from a recent, representative production dataset and update the monitoring schedule to use the new baseline.
Data quality monitors flag when observed statistics fall outside the constraints derived from the baseline. When a feature's production distribution legitimately diverges from training, the correct fix is to re-establish the baseline from representative recent data so constraints reflect the new normal, rather than muting or deleting the check.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Regenerate the baseline statistics and constraints from a recent, representative production dataset and update the monitoring schedule to use the new baseline.
Why this is correct
Model Monitor compares incoming data against the statistics and constraints stored in the baseline. Because the production distribution of promo_code legitimately differs from the training data, the old constraints encode a stale expectation. Rebuilding the baseline from recent representative production data realigns the constraint thresholds with current behavior, so violations stop without turning monitoring off.
- ✗
Delete the monitoring schedule and create a new one with a longer monitoring interval so that fewer violations accumulate over time.
Why it's wrong here
Changing the monitoring interval only alters how often the monitor runs; each run still compares data against the same baseline constraints and will still report the promo_code distribution shift. This suppresses the cadence of alerts rather than correcting the false positive, and it leaves a monitoring gap that defeats the purpose of the control.
- ✗
Edit the constraints JSON file in Amazon S3 to remove the promo_code entry, then leave the schedule unchanged.
Why it's wrong here
Removing the feature from the constraints file stops violations for that column, but it also silently drops a monitored feature rather than correcting the expectation. Any future genuine anomaly in promo_code would go undetected, and hand-editing generated constraint files is error-prone and not reproducible when the monitor is rebuilt.
- ✗
Enable explainability monitoring on the schedule so that feature attribution drift accounts for the change in promo_code.
Why it's wrong here
Explainability monitoring tracks changes in feature attribution values using SHAP, which is a different signal from data quality constraint violations. Turning it on adds a second monitor and does not modify the data quality constraints that are producing the promo_code alerts, so the false positives would continue alongside the new metrics.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
This MLA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLA-C01 exam.