AIF-C01 Practice Question: Security, Compliance, and Governance for AI Solutions
A data science team is deploying a model using Amazon SageMaker. They need to monitor the model for bias after it is deployed. Which AWS service or feature should they use?
⚠ Common exam trap
AIF-C01 often tests the boundary between Clarify (bias and explainability) and Model Monitor (drift detection); candidates pick Model Monitor because it also sounds like a monitoring service, but bias-specific metrics come from Clarify.
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 machine learning models, including pre-training bias metrics on datasets and post-training bias metrics on deployed model predictions. It integrates with SageMaker Model Monitor to continuously detect bias drift in production.
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 Bedrock Guardrails
Why it's wrong here
Bedrock Guardrails filter prompts and responses for harmful content on Bedrock models, not SageMaker endpoints, and do not compute bias metrics. Guardrails suit content-safety enforcement in generative applications, whereas post-deployment bias monitoring requires SageMaker Model Monitor's bias drift detection.
- ✓
Amazon SageMaker Clarify
Why this is correct
SageMaker Clarify runs bias metrics such as disparate impact against deployed endpoints and emits them to CloudWatch, detecting bias after deployment. This satisfies the post-deployment monitoring requirement, which pre-training Clarify analysis alone would not cover.
- ✗
Amazon SageMaker Model Monitor
Why it's wrong here
SageMaker Model Monitor detects data drift and quality issues in deployed endpoints; it does not compute bias metrics such as disparate impact, which require SageMaker Clarify. It is tempting because Model Monitor is the post-deployment monitoring feature, but it would be correct for catching feature drift rather than measuring bias.
- ✗
AWS CloudTrail
Why it's wrong here
CloudTrail records AWS API activity and account actions, not model predictions or their statistical properties, so it cannot detect bias drift. It is the correct choice for auditing who invoked SageMaker endpoints or changed configuration, not for monitoring inference data.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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.