AWS Certified Machine Learning Engineer – Associate (MLA-C01) — Question 7

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?

Answer options

• A. Create a separate Amazon Elastic Container Registry (Amazon ECR) repository for each model.
• B. Use Amazon Elastic Container Registry (Amazon ECR) and unique tags for each model version.
• C. Use the SageMaker Model Registry and model groups to catalog the models.
• D. Use the SageMaker Model Registry and unique tags for each model version.

Correct answer: C

Explanation

The correct answer is C because the SageMaker Model Registry is specifically designed to manage and catalog models and their versions efficiently. Options A and B introduce unnecessary complexity and operational overhead by requiring separate repositories or unique tags, while option D does not leverage the organizational capabilities of model groups.