Topic 1 Question 268
A company wants to enhance audits for its machine learning (ML) systems. The auditing system must be able to perform metadata analysis on the features that the ML models use. The audit solution must generate a report that analyzes the metadata. The solution also must be able to set the data sensitivity and authorship of features.
Which solution will meet these requirements with the LEAST development effort?
Use Amazon SageMaker Feature Store to select the features. Create a data flow to perform feature-level metadata analysis. Create an Amazon DynamoDB table to store feature-level metadata. Use Amazon QuickSight to analyze the metadata.
Use Amazon SageMaker Feature Store to set feature groups for the current features that the ML models use. Assign the required metadata for each feature. Use SageMaker Studio to analyze the metadata.
Use Amazon SageMaker Features Store to apply custom algorithms to analyze the feature-level metadata that the company requires. Create an Amazon DynamoDB table to store feature-level metadata. Use Amazon QuickSight to analyze the metadata.
Use Amazon SageMaker Feature Store to set feature groups for the current features that the ML models use. Assign the required metadata for each feature. Use Amazon QuickSight to analyze the metadata.
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コメント(9)
- 正解だと思う選択肢: B
I think it's B
👍 1ADVIT2023/07/07 - 正解だと思う 選択肢: B👍 1awsarchitect52023/07/24
- 正解だと思う選択肢: D
This solution meets the requirements with the least development effort because it uses Amazon SageMaker Feature Store, which is a fully managed service that makes it easy to store and manage feature metadata. Amazon SageMaker Feature Store also provides built-in functionality for analyzing feature metadata, so there is no need to create custom algorithms or data flows.
👍 1Mickey3212023/08/04
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