Selected Answer: C https://docs.aws.amazon.com/sagemaker/latest/dg/model-registry-models.html "Each model package in a Model Group maps to a trained model. The version of each model package is a numeric value that starts at 1 and increases with each new model package added to a Model Group. For example, if 5 model packages are added to a Model Group, the model package versions will be 1, 2, 3, 4, and 5."

Selected Answer: C https://aws.amazon.com/blogs/machine-learning/centralize-model-governance-with-sagemaker-model-registry-resource-access-manager-sharing/

Selected Answer: C The correct answer is C. Use the SageMaker Model Registry and model groups to catalog the models. SageMaker Model Registry is built specifically for managing ML models within the SageMaker ecosystem. It offers built-in versioning and cataloging features for ML models. Model groups in the Model Registry let you organize related models logically. It works smoothly with other SageMaker components like training, deployment, and monitoring. This solution has the least operational overhead because it's a native SageMaker feature built for this exact use case. It includes features like model approval workflows, which are important for managing models in production environments.

Selected Answer: C C: The SageMaker Model Registry groups models into Model Package Groups, tracks versions as Model Packages, and can optionally roll groups up into Collections. This structure provides solid versioning and manageability for trained models. https://docs.aws.amazon.com/sagemaker/latest/dg/model-registry-models.html

Selected Answer: C SageMaker Model Registry is built specifically for managing ML models within the SageMaker ecosystem. It offers built-in versioning and cataloging features for ML models. Model groups in the Model Registry let you organize related models logically. It works smoothly with other SageMaker components like training, deployment, and monitoring. This solution has the least operational overhead because it's a native SageMaker feature built for this exact use case. It includes features like model approval workflows, which are important for managing models in production environments.

Selected Answer: C A. No, ECR is used to store container images B. No, ECR is used to store container images C. Yes D. No, Each model package in a Model Group corresponds to a trained model. The version of each model package is a numerical value that starts at 1 and is incremented with each new model package added to a Model Group - https://docs.aws.amazon.com/sagemaker/latest/dg/model-registry-models.html

Selected Answer: C Amazon SageMaker Model Registry is designed to centrally manage and catalog models, including versioning, approval workflows, and deployment history. It makes it simpler to handle different model versions, which matches the company's need for a central model registry.

Selected Answer: C Amazon SageMaker Model Registry creates Catalog models for production and manages model versions.

Selected Answer: C C. Use the SageMaker Model Registry and model groups to catalog the models. This approach uses SageMaker's built-in features, cutting down the need for external systems like ECR and reducing operational complexity by using model groups for structured versioning.

Selected Answer: C least operational is the keyword here

Selected Answer: C Use model group

The sage maker model has the capabilities to deliver on model monitoring and registry