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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 orchestrates training workflows and CI/CD steps, not model cards or deployment history records. It is tempting because pipelines do automate model building and versioning artefacts, but the stem asks for documented intended use, performance and limitations shared with stakeholders, which SageMaker Model Cards and Model Registry provide.
- ✗
Amazon SageMaker Clarify
Why it's wrong here
Clarify detects bias and explains feature attributions; it does not produce model cards or version histories. It is tempting because documenting intended use and limitations sounds like explainability, but Clarify's output is statistical analysis, not the governance documentation and deployment tracking the stem requires.
- ✗
Amazon SageMaker Studio
Why it's wrong here
Studio is an integrated development environment for building and running notebooks; it stores no model documentation or deployment lineage. It is tempting because teams work in Studio daily, but the stem requires model cards and a registry tracking versions and deployments, not an IDE.
- ✓
Amazon SageMaker Model Cards
Why this is correct
Amazon SageMaker Model Cards capture intended use, performance metrics, and limitations in a structured document, directly satisfying the stakeholder documentation requirement. Model Registry separately handles versioning and deployment history, so Model Cards alone address only the documentation half of the stem's two-part need.
- ✓
Amazon SageMaker Model Registry
Why this is correct
Amazon SageMaker Model Registry satisfies the documentation and tracking constraints: it stores model cards capturing intended use, performance metrics and limitations for stakeholders, while maintaining versioned model packages with approval status and deployment history. This directly addresses both the sharing requirement and the version-tracking requirement in the stem.
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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.