AIF-C01 Practice Question: Security, Compliance, and Governance for AI Solutions
A company has deployed a machine learning model using Amazon SageMaker and wants to monitor the model for bias over time. Which SageMaker feature should they use to detect bias in the model's predictions 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
✓
SageMaker Clarify
SageMaker Clarify is designed to detect bias in ML models both before and after deployment. It can analyze predictions to identify potential bias against certain groups.
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 Debugger
Why it's wrong here
SageMaker Debugger captures tensors and metrics during training to diagnose convergence issues, not post-deployment bias. SageMaker Clarify is the feature that detects bias in deployed model predictions. Debugger is tempting when troubleshooting training anomalies such as vanishing gradients, where its built-in rules and profiling reports are the right tool.
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SageMaker Model Monitor
Why it's wrong here
SageMaker Model Monitor tracks data drift and quality metrics over time, but it does not evaluate prediction outcomes for bias against protected attributes. The scenario requires detecting bias in model predictions post-deployment, which demands fairness metrics like disparate impact or demographic parity. Model Monitor is tempting because it monitors deployed models for degradation, making it a natural first thought for any post-deployment monitoring task; it would be correct if the question asked about detecting covariate shift or feature distribution changes rather than bias.
- ✓
SageMaker Clarify
Why this is correct
SageMaker Clarify detects bias in deployed models through bias metrics on predictions and features, and integrates with Model Monitor for ongoing post-deployment checks. This satisfies the requirement to monitor bias over time rather than only during training.
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SageMaker Role Manager
Why it's wrong here
SageMaker Role Manager governs IAM permissions for users and execution roles; it performs no statistical analysis of predictions, so it cannot measure bias drift. It is tempting because it is a SageMaker governance feature, and would be the right choice when the task is scoping least-privilege access for data scientists.
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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.