MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
A machine learning engineer is monitoring a deployed model for data drift. The input features are a mix of categorical and numerical columns. The baseline is from the training data. Which SageMaker Model Monitor feature should they enable to detect changes in the distribution of each feature over time?
⚠ Common exam trap
MLA-C01 often tests the confusion between data quality monitoring and model quality monitoring — candidates pick C because 'model' sounds right, but model quality needs ground-truth labels and measures accuracy, while data quality measures input feature distributions.
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
✓
Data quality monitoring
Data quality monitoring in SageMaker Model Monitor compares the statistical distribution of each input feature (both numerical and categorical) against a baseline computed from the training data, detecting drift in feature distributions over time. It supports categorical and numerical columns and is the correct feature for detecting per-feature distribution changes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Bias drift monitoring
Why it's wrong here
Bias drift monitoring measures changes in bias metrics such as demographic parity across groups, requiring a sensitive attribute and predicted labels. It detects fairness shifts, not per-feature distribution changes, so it is correct when regulatory bias auditing is the goal rather than general data drift.
- ✓
Data quality monitoring
Why this is correct
Data quality monitoring computes distribution metrics per feature against the training baseline, handling numerical and categorical columns separately, and emits violations when distributions shift. This satisfies the requirement to detect per-feature distribution changes over time.
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Model quality monitoring
Why it's wrong here
Model quality monitoring compares predictions against ground-truth labels for accuracy, precision and recall, so it cannot detect shifts in input feature distributions. It is the right choice when labelled outcomes arrive and you want to track prediction performance degradation over time.
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Feature attribution drift monitoring
Why it's wrong here
Feature attribution drift monitoring tracks changes in the contribution each feature makes to predictions, not the distribution of raw feature values. It is tempting because it detects model-behaviour drift, and would be correct when the concern is explainability shifting rather than input data distributions, which Data Quality monitoring covers.
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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
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