easyMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: Maintain multiple versions of a trained model in…
A company wants to maintain multiple versions of a trained model in a central repository and track metadata such as training metrics, hyperparameters, and approval status. Which SageMaker feature should they use?
⚠ Common exam trap
Test-takers frequently confuse SageMaker Experiments (which tracks training runs) with the Model Registry (which manages model versions and approvals), leading them to select Experiments when the question explicitly asks for a central repository with versioning and approval workflows.
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 is specifically designed to serve as a central repository for managing multiple versions of trained models, tracking metadata such as training metrics, hyperparameters, and approval status. It integrates with SageMaker Pipelines and Experiments to automate model governance, enabling versioning, approval workflows, and lineage tracking.
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 Pipelines
Why it's wrong here
Pipelines automate ML workflows, but are not a model repository.
- ✗
SageMaker Feature Store
Why it's wrong here
Feature Store manages features, not models.
- ✓
SageMaker Model Registry
Why this is correct
Correct. Model Registry provides a central repository for model versions, metadata, and approval status.
- ✗
SageMaker Experiments
Why it's wrong here
Experiments track training runs and compare metrics, but not model versioning or approval.
- ✗
SageMaker Studio
Why it's wrong here
Studio is an IDE, not a model versioning service.
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