AIF-C01 Practice Question: Security, Compliance, and Governance for AI Solutions
A company is implementing an AI governance framework for their machine learning models deployed on Amazon SageMaker. Which THREE actions should they include to manage the model lifecycle effectively? (Select THREE.)
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
✓
Enable Amazon CloudWatch alarms for model accuracy metrics
Model versioning tracks changes, monitoring for drift ensures performance, and a decommissioning process retires outdated models. These are key lifecycle management practices.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable Amazon CloudWatch alarms for model accuracy metrics
Why this is correct
Monitoring for drift is essential; CloudWatch can alert on accuracy drops.
- ✗
Automatically delete all previous model versions after deployment
Why it's wrong here
Deleting models prevents rollback and audit; best practice is to archive.
- ✓
Create a decommissioning policy that retires old models
Why this is correct
A formal decommissioning process ensures models are properly retired.
- ✗
Use AWS CloudTrail to track model inference requests
Why it's wrong here
CloudTrail tracks API calls, not model lifecycle management.
- ✓
Use SageMaker Model Registry to version models
Why this is correct
Model Registry provides version control and approval workflows.
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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.