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AIF-C01 Practice Question: Security, Compliance, and Governance for AI Solutions

An organization is implementing governance for machine learning models using SageMaker. They need to track model versions, monitor for drift after deployment, and automatically decommission models that have been deprecated for over 30 days. Which THREE services or features should they use?

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 Model Registry

SageMaker Model Registry tracks versions, Model Monitor detects drift, and SageMaker Projects with MLOps can automate decommissioning. Clarify is for bias, not lifecycle management. Ground Truth is for labeling.

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 Ground Truth

    Why it's wrong here

    Ground Truth is for data labeling, not model governance.

  • Amazon SageMaker Clarify

    Why it's wrong here

    Clarify is for bias detection and explainability, not lifecycle management or drift.

  • Amazon SageMaker Model Registry

    Why this is correct

    Model Registry manages model versions and lifecycle stages.

  • Amazon SageMaker Projects (MLOps templates)

    Why this is correct

    Projects can automate deployment and decommissioning workflows.

  • Amazon SageMaker Model Monitor

    Why this is correct

    Model Monitor detects data drift and quality issues after deployment.

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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.