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.
- ✗
SageMaker Model Monitor – Model Quality
Why it's wrong here
Model quality monitors overall model performance metrics, not fairness across groups.
- ✗
SageMaker Model Monitor – Feature Attribution Drift
Why it's wrong here
Feature attribution drift monitors changes in SHAP values, not bias metrics.
- ✓
SageMaker Clarify (post-deployment bias monitoring)
Why this is correct
SageMaker Clarify can be configured to run bias monitoring jobs that detect drift in fairness metrics after deployment.
- ✗
SageMaker Model Monitor – Data Quality
Why it's wrong here
Data quality monitors input feature distributions, not bias metrics.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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