MLA-C01 Deployment and Orchestration of ML Workflows Practice Question

A company wants to version and track ML models, with an approval workflow for promoting models from staging to production. Which SageMaker feature should they use?

⚠ Common exam trap

Watch out — candidates often confuse SageMaker Pipelines (which orchestrates the workflow) with SageMaker Model Registry (which manages the model versions and approvals), but the question specifically asks for the feature that handles versioning and approval workflow, not the orchestration of the pipeline itself.

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 repository to catalog, version, and manage ML models, and it supports approval workflows (e.g., PendingApproval, Approved, Rejected) to promote models from staging to production. This directly addresses the requirement for version tracking and an approval gate for model promotion.

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 Monitor
Why it's wrong here
Model Monitor detects drift, not versioning.
✗
SageMaker Experiments
Why it's wrong here
Experiments track training runs, not model versioning and approvals.
✗
SageMaker Pipelines
Why it's wrong here
Pipelines orchestrate steps, but model versioning is handled by the Model Registry.
✓
SageMaker Model Registry
Why this is correct
Model Registry offers versioning, approval workflow, and deployment to production.

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MLA-C01 Deployment and Orchestration of ML Workflows Practice Question

Why each option matters

Option-by-option breakdown

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