AIF-C01 Practice Question: Security, Compliance, and Governance for AI Solutions
A machine learning team wants to detect bias in a deployed model's predictions on new data. They use Amazon SageMaker. Which service should they use to generate bias reports after deployment?
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
✓
Amazon SageMaker Clarify
Amazon SageMaker Clarify provides bias detection and explainability for ML models, both during training and after deployment. SageMaker Model Monitor detects data drift but not bias. SageMaker Debugger is for training debugging. SageMaker Role Manager is for managing IAM roles.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Amazon SageMaker Clarify
Why this is correct
Amazon SageMaker Clarify generates post-deployment bias reports by monitoring live endpoint traffic and computing metrics such as disparate impact against baseline data. This satisfies the stem's requirement to detect bias in predictions on new data after deployment, which pre-training Clarify analysis alone cannot cover.
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Amazon SageMaker Debugger
Why it's wrong here
Debugger captures tensors and training metrics to diagnose convergence and training-job anomalies, not post-deployment bias. It tempts because it inspects model internals during training, but bias on new inference data is measured by Clarify against a baseline, not by Debugger hooks.
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Amazon SageMaker Model Monitor
Why it's wrong here
Model Monitor detects data drift and quality violations on captured endpoint traffic, but its bias monitoring requires a configured baseline and runs on live inference data rather than generating standalone post-deployment bias reports. SageMaker Clarify produces those bias reports. Model Monitor would be right for ongoing drift alerting after Clarify establishes the baseline.
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Amazon SageMaker Role Manager
Why it's wrong here
Role Manager builds IAM execution roles and least-privilege permissions for SageMaker users, not bias metrics. It tempts because it governs access around ML workflows, but detecting bias in deployed predictions requires monitoring statistics such as disparate impact computed against live data.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AIF-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 AIF-C01 exam.