MLA-C01 · Question #1
Case Study A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central…
The correct answer is C. Use the SageMaker Model Registry and model groups to catalog the models. Option C is correct because SageMaker Model Registry is a purpose-built feature within SageMaker specifically designed for cataloging, versioning, and managing ML models using model groups - each group holds all versions of a given model, making governance, approval workflows…
Question
Case Study A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring. The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3. The company needs to use the central model registry to manage different versions of models in the application. Which action will meet this requirement with the LEAST operational overhead?
Options
- ACreate a separate Amazon Elastic Container Registry (Amazon ECR) repository for each model.
- BUse Amazon Elastic Container Registry (Amazon ECR) and unique tags for each model version.
- CUse the SageMaker Model Registry and model groups to catalog the models.
- DUse the SageMaker Model Registry and unique tags for each model version.
How the community answered
(28 responses)- A7% (2)
- B14% (4)
- C75% (21)
- D4% (1)
Explanation
Option C is correct because SageMaker Model Registry is a purpose-built feature within SageMaker specifically designed for cataloging, versioning, and managing ML models using model groups - each group holds all versions of a given model, making governance, approval workflows, and deployment straightforward with zero additional infrastructure to manage.
Why the distractors are wrong:
- A - Creating a separate ECR repository per model is designed for container images, not ML model artifacts; it adds significant management overhead and misses built-in lineage/approval features.
- B - Using ECR with unique tags is still the wrong tool (container registry vs. model registry) and tagging alone provides no governance, approval workflows, or model metadata tracking.
- D - SageMaker Model Registry is the right service, but tags alone are a weak versioning mechanism compared to model groups, which provide structured versioning with status tracking (Approved/Rejected/PendingManualApproval) out of the box.
Memory tip: Think "model groups = filing cabinet drawers" - each drawer (group) holds all versions of one model type, and SageMaker Model Registry is the filing cabinet built specifically for this job. If the question mentions a central model registry and versioning, the answer is always SageMaker Model Registry with model groups, not ECR (which is for Docker images) and not just tags (which lack structure).
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