MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
A hospital deploys a model to predict patient readmission risk. To comply with regulations, they must ensure that the model's predictions do not show bias against any demographic group over time. Which service should they use for ongoing monitoring?
⚠ Common exam trap
A common mix-up: candidates confuse SageMaker Model Monitor (which tracks data drift) with SageMaker Clarify (which tracks bias), leading candidates to choose Model Monitor because they think 'monitoring' covers all aspects of model health, but bias detection requires a separate, specialized tool.
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
SageMaker Clarify is the correct service because it is specifically designed to detect bias in ML model predictions and can be configured for ongoing monitoring. It provides bias metrics (e.g., difference in positive proportion, disparate impact) and can run on a schedule to continuously evaluate predictions against demographic groups, ensuring regulatory compliance 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 Clarify
Why this is correct
SageMaker Clarify provides bias detection with configurable metrics such as demographic parity difference, and its monitoring schedules run continuously against live endpoint traffic, satisfying the requirement for ongoing bias monitoring across demographic groups rather than one-off analysis.
- ✗
AWS Audit Manager
Why it's wrong here
AWS Audit Manager automates evidence collection against compliance frameworks; it cannot measure prediction bias across demographic groups. It is tempting as a regulatory compliance tool, and would be correct for continuously gathering audit artefacts and control evidence for frameworks such as HIPAA.
- ✗
SageMaker Model Monitor
Why it's wrong here
SageMaker Model Monitor detects data drift and quality deviations in deployed endpoints, not bias across demographic groups. It is tempting because it provides ongoing production monitoring, and it would be correct for tracking feature distribution shifts or accuracy degradation over time.
- ✗
Amazon Macie
Why it's wrong here
Macie discovers and classifies sensitive data in S3, such as personally identifiable information; it does not evaluate model predictions for demographic bias. Macie is correct for data-privacy auditing, whereas bias drift monitoring requires a service that tracks model performance across groups.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLA-C01 exam.