MLS-C01 Question #302: Real Exam Question with Answer & Explanation

Question

A machine learning (ML) engineer has created a feature repository in Amazon SageMaker Feature Store for the company. The company has AWS accounts for development, integration, and production. The company hosts a feature store in the development account. The company uses Amazon S3 buckets to store feature values offline. The company wants to share features and to allow the integration account and the production account to reuse the features that are in the feature repository. Which combination of steps will meet these requirements? (Choose two.)

Options

• ACreate an IAM role in the development account that the integration account and production
• BShare the feature repository that is associated the S3 buckets from the development account to
• CUse AWS Security Token Service (AWS STS) from the integration account and the production
• DSet up S3 replication between the development S3 buckets and the integration and production S3
• ECreate an AWS PrivateLink endpoint in the development account for SageMaker.

Explanation

The company needs to share a SageMaker Feature Store from a development account with integration and production accounts for feature reuse.

Common mistakes.

• C. AWS Security Token Service (AWS STS) is used to obtain temporary credentials to assume an IAM role, but it is not the mechanism for creating the cross-account access policy itself.
• D. S3 replication only duplicates the offline feature data; it does not share the SageMaker Feature Group definitions or provide access to the online store, which are essential for feature reuse.
• E. Creating an AWS PrivateLink endpoint provides private network access to SageMaker services from within a VPC, but it does not enable cross-account sharing of the Feature Store itself.

Concept tested. Cross-account sharing of SageMaker Feature Store

Reference. https://docs.aws.amazon.com/sagemaker/latest/dg/feature-store-cross-account-sharing.html

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