MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
A company deploys a model for fraud detection. They need to monitor for bias after deployment, specifically whether the model's false positive rate changes across demographic groups over time. Which SageMaker feature should they use?
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 (post-deployment bias monitoring)
SageMaker Clarify provides post-deployment bias monitoring by analyzing predictions against ground truth labels for defined facets. It can track metrics like false positive rate differences over time.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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SageMaker Model Monitor – Model Quality
Why it's wrong here
Model Quality Monitor computes accuracy, precision, recall and F1 against ground truth, but not false positive rate broken down by demographic group. It is tempting because it is the standard post-deployment performance monitor, and would be correct if the requirement were overall accuracy degradation rather than bias across groups.
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SageMaker Model Monitor – Feature Attribution Drift
Why it's wrong here
Feature Attribution Drift tracks changes in the contribution each input feature makes to predictions, not outcome fairness metrics. It is tempting because it detects data and concept drift, which is the right choice when you need to know whether the model's inputs have shifted, but it cannot compute false positive rates per demographic group.
- ✓
SageMaker Clarify (post-deployment bias monitoring)
Why this is correct
SageMaker Clarify's post-deployment bias monitoring continuously evaluates live endpoint traffic, computing metrics such as false positive rate disparity across demographic groups over time. This directly satisfies the requirement to detect bias drift after deployment, which static pre-training analysis cannot address.
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SageMaker Model Monitor – Data Quality
Why it's wrong here
Data Quality monitors feature distributions and data drift, not false positive rates across demographic groups. It is tempting because it is a genuine Model Monitor baseline type, and it would be correct if the requirement were detecting changes in input data schema or statistics.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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