Q91 — AWS AIF-C01 Ch.3

Question 91 of 100 | ← Chapter 3

A company's users develop ML models in Amazon SageMaker Canvas. After development, these models need to be reviewed and approved by the data science team working in SageMaker Studio. Which AWS service enables the least operational overhead to provide data scientists with access to the ML models?

• A. SageMaker JumpStart
• B. SageMaker Model Registry ✓
• C. Amazon DynamoDB
• D. Amazon Elastic Container Registry (ECR)

Correct Answer: B. SageMaker Model Registry

Explanation

Amazon SageMaker Model Registry is a dedicated service designed for managing and sharing machine learning models, enabling model versioning, auditing, and governance. In this scenario, the data science team needs to review and approve ML models developed by users in SageMaker Canvas within SageMaker Studio. Using SageMaker Model Registry allows data scientists to easily access these models while maintaining tracking of model versions and changes, thereby fulfilling the review and approval workflow with minimal operational overhead.