MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A data scientist wants to version and manage trained models, require approval before deployment, and enable cross-account deployment. Which SageMaker feature provides these capabilities?
⚠ Common exam trap
Watch out — candidates often confuse SageMaker Pipelines (which orchestrates the ML workflow) with Model Registry (which manages model versions and approvals), but Pipelines lacks native versioning and approval gatekeeping, while Model Registry is specifically designed for those governance tasks.
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 catalog for versioning trained models, supports approval workflows (e.g., pending, approved, rejected) to gate deployment, and enables cross-account deployment by sharing model package ARNs across AWS accounts via AWS Resource Access Manager (RAM) or cross-account IAM roles. This directly satisfies all three requirements: versioning, approval before deployment, and cross-account deployment.
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