MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
A company uses SageMaker Model Monitor to detect bias drift in their real-time inference endpoint. They have collected ground truth labels and want to monitor for bias across different demographic groups. Which type of monitoring should they configure?
⚠ Common exam trap
MLA-C01 often tests the distinction between different Model Monitor types, and candidates may confuse bias drift with feature attribution drift or model quality monitoring.
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
✓
SageMaker Clarify – Bias Drift Monitoring
SageMaker Clarify – Bias Drift Monitoring is the correct choice because it specifically monitors bias metrics over time by comparing ground truth labels with model predictions across different demographic groups. It detects changes in bias metrics such as disparate impact, which is exactly what the company needs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
SageMaker Model Monitor – Feature Attribution Drift Monitoring
Why it's wrong here
Feature Attribution Drift Monitoring tracks changes in which features contribute to predictions, using explainability output rather than group fairness statistics. It is tempting because it also uses a baseline, and would be correct for detecting shifts in feature importance that silently alter model behaviour without labelled outcome data.
- ✓
SageMaker Clarify – Bias Drift Monitoring
Why this is correct
SageMaker Clarify bias drift monitoring compares live inference data against ground truth labels, computing bias metrics across demographic groups and alerting when they deviate from baseline. This satisfies the requirement to detect bias drift for specific groups.
- ✗
SageMaker Model Monitor – Model Quality Monitoring
Why it's wrong here
Model Quality Monitoring compares predictions against ground truth labels for accuracy, precision and similar metrics, but does not compute bias metrics across demographic groups. It is tempting because ground truth labels are available, and it would be correct for detecting concept drift or degrading prediction accuracy after deployment.
- ✗
SageMaker Model Monitor – Data Quality Monitoring
Why it's wrong here
Data Quality Monitoring compares inference input statistics against a baseline, covering schema and distribution shifts, not label-based fairness metrics across demographic groups. It is tempting because it is the default Model Monitor type, and would be correct for catching feature drift or missing-value problems in incoming request payloads.
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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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