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AIF-C01 Practice Question: A data science team wants to document and share…
A data science team wants to document and share their model's intended use, performance, and limitations with stakeholders. They also need to track the model's version and deployment history. Which TWO AWS 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 Cards
SageMaker Model Cards provide standardized documentation for transparency. SageMaker Model Registry tracks model versions, deployment stages, and metadata. SageMaker Pipelines is for ML workflows, not documentation or version tracking. SageMaker Studio is an IDE. SageMaker Clarify is for bias and explainability.
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 Pipelines
Why it's wrong here
Pipelines orchestrate ML workflows, not documentation or version tracking.
- ✗
Amazon SageMaker Clarify
Why it's wrong here
Clarify is for bias detection and explainability, not for documentation or version tracking.
- ✗
Amazon SageMaker Studio
Why it's wrong here
Studio is an IDE for ML development, not a documentation or registry service.
- ✓
Amazon SageMaker Model Cards
Why this is correct
Model Cards document intended use, performance, and limitations.
- ✓
Amazon SageMaker Model Registry
Why this is correct
Model Registry tracks versions, approvals, and deployment history.
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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.