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?
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 offers post-deployment bias monitoring that uses ground truth labels to compute bias metrics (e.g., difference in positive outcome rates) 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 – Feature Attribution Drift Monitoring
Why it's wrong here
Feature attribution drift monitors SHAP values, not bias.
- ✓
SageMaker Clarify – Bias Drift Monitoring
Why this is correct
Clarify's bias drift monitoring uses ground truth labels to compute bias metrics for demographic groups.
- ✗
SageMaker Model Monitor – Model Quality Monitoring
Why it's wrong here
Model quality monitoring tracks prediction accuracy against ground truth, not bias metrics.
- ✗
SageMaker Model Monitor – Data Quality Monitoring
Why it's wrong here
Data quality monitoring checks input data distribution, not bias.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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