Question 30
Domain 3: Deployment and Orchestration of ML WorkflowsA company that has hundreds of data scientists is using Amazon SageMaker to create ML models. The models are in model groups in the SageMaker Model Registry. The data scientists are grouped into three categories: computer vision, natural language processing (NLP), and speech recognition. An ML engineer needs to implement a solution to organize the existing models into these groups to improve model discoverability at scale. The solution must not affect the integrity of the model artifacts and their existing groupings. Which solution will meet these requirements?
Correct answer: D
Explanation
Amazon SageMaker Model Registry supports organizing model groups into higher-level collections for discoverability without changing the underlying model artifacts. Creating a collection for each category and moving the existing model groups into them preserves the model artifacts and their existing groupings while adding the needed structure for computer vision, NLP, and speech recognition.
Why each option is right or wrong
A. Create a custom tag for each of the three categories. Add the tags to the model packages in the SageMaker Model Registry.
Tags help label individual resources, but they do not provide the intended higher-level registry organization.
B. Create a model group for each category. Move the existing models into these category model groups.
Moving models into new groups changes existing groupings, violating the requirement to preserve current organization.
C. Use SageMaker ML Lineage Tracking to automatically identify and tag which model groups should contain the models.
ML lineage tracks relationships and provenance, not category-based registry organization for discoverability.
D. Create a Model Registry collection for each of the three categories. Move the existing model groups into the collections.
Amazon SageMaker Model Registry supports hierarchical organization by using collections as a higher-level grouping construct above model groups, so the engineer can add discoverability without modifying the registered model artifacts themselves. In this scenario, creating 3 collections aligns the existing model groups to the 3 business categories, and moving the groups into those collections preserves the underlying model package versions and their current group membership rather than re-registering or altering artifacts.