easyMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A data scientist wants to version control trained…
A data scientist wants to version control trained models and manage approvals for deployment. Which SageMaker feature should they use?
⚠ Common exam trap
Many exam-takers confuse SageMaker Experiments (which tracks training runs) with the Model Registry (which manages model versions and approvals), leading them to pick Experiments because they think 'version control' refers to experiment iterations rather than model artifacts.
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 feature because it is specifically designed for versioning trained models, tracking their metadata (e.g., training job, metrics), and managing approval workflows for deployment stages (e.g., Pending, Approved, Rejected). This directly addresses the data scientist's need to version control models and manage deployment approvals.
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 Model Registry.
Why this is correct
Model Registry provides version control for models and supports approval workflows for deployment.
- ✗
SageMaker Experiments.
Why it's wrong here
SageMaker Experiments tracks training runs and parameters, but does not manage model versions or approvals.
- ✗
SageMaker Feature Store.
Why it's wrong here
Feature Store manages features for training and inference, not model versions.
- ✗
SageMaker Ground Truth.
Why it's wrong here
Ground Truth is a data labeling service, not for model management.
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