AWS Certified Machine Learning – Specialty — Question 263

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?

Answer options

• A. 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.
• B. 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.
• C. 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.
• D. 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.

Correct answer: D

Explanation

Option D is the best choice because it directly addresses the requirements of setting feature groups and attaching metadata with minimal complexity, utilizing Amazon QuickSight for analysis. Options A and C involve additional steps and resources, such as creating a DynamoDB table and using custom algorithms, which increases development effort. Option B, while similar to D, does not include the use of Amazon QuickSight for analysis, which is required.