Topic 1 Question 275
2 つ選択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?
Create an IAM role in the development account that the integration account and production account can assume. Attach IAM policies to the role that allow access to the feature repository and the S3 buckets.
Share the feature repository that is associated the S3 buckets from the development account to the integration account and the production account by using AWS Resource Access Manager (AWS RAM).
Use AWS Security Token Service (AWS STS) from the integration account and the production account to retrieve credentials for the development account.
Set up S3 replication between the development S3 buckets and the integration and production S3 buckets.
Create an AWS PrivateLink endpoint in the development account for SageMaker.
ユーザの投票
コメント(2)
A & B Create an IAM role in the dev account and share the features repository using AWS RAM
👍 3usamazubairi2023/12/14- 正解だと思う選択肢: AB
A: By creating an IAM role in the development account that the integration and production accounts can assume, you establish a trust relationship between the accounts. You can attach IAM policies to the role that grant the necessary permissions to access the feature repository and S3 buckets.
B: AWS Resource Access Manager (AWS RAM) enables resource sharing across AWS accounts. By sharing the feature repository associated with the S3 buckets using AWS RAM, you allow the integration and production accounts to access and reuse the features.
👍 2xiaoeason2023/12/15
シャッフルモード