AIF-C01 Guidelines for Responsible AI Practice Question
A large enterprise has multiple teams deploying ML models on AWS. To ensure governance and accountability, they need to enforce that all models pass a fairness review before production deployment. Which SageMaker feature should they use to implement this approval workflow?
⚠ Common exam trap
Many exam-takers confuse SageMaker Model Registry with SageMaker Model Monitor, mistakenly thinking monitoring covers pre-deployment fairness checks, when in fact Model Monitor only handles post-deployment observability.
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 Model Registry
SageMaker Model Registry is the correct choice because it provides a centralized catalog for managing ML models, including versioning, approval status, and metadata. It supports approval workflows by allowing you to define model groups, set approval statuses (e.g., PendingApproval, Approved, Rejected), and integrate with CI/CD pipelines to enforce that only approved models are deployed to 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.
- ✗
SageMaker Studio
Why it's wrong here
SageMaker Studio is an integrated development environment for building, training and debugging models; it holds no approval gate that blocks deployment. It is tempting because teams already work there. SageMaker Model Registry supplies model approval status, letting a fairness review be enforced before production deployment.
- ✗
SageMaker Experiments
Why it's wrong here
SageMaker Experiments tracks runs, parameters and metrics for comparison during development; it records no approval decision and cannot block deployment. It is tempting because it centralises model metadata. SageMaker Model Registry holds the approval status that a fairness review must set before production deployment proceeds.
- ✗
SageMaker Model Monitor
Why it's wrong here
Model Monitor detects data drift and quality deviations on deployed endpoints; it observes running models rather than gating them. It is tempting because fairness relates to model behaviour. SageMaker Model Registry provides the approval status workflow that must be satisfied before a model version reaches production.
- ✓
SageMaker Model Registry
Why this is correct
SageMaker Model Registry enforces the approval workflow: models are registered as versioned model packages, and a pending manual approval status blocks deployment until a fairness review is completed. This directly satisfies the enterprise's governance requirement that every model pass review before reaching production.
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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.