Amazon Exam MLS-C01 Topic 9 Question 84 Discussion

Actual exam question for Amazon's MLS-C01 exam

Question #: 84
Topic #: 9

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?

AUse 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.

BUse 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.

CUse 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.

DUse 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.

Show Suggested Answer

Suggested Answer: D

The solution that will meet the requirements with the least development effort is to 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, and use Amazon QuickSight to analyze the metadata. This solution can leverage the existing AWS services and features to perform feature-level metadata analysis and reporting.

Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, update, search, and share machine learning (ML) features. The service provides feature management capabilities such as enabling easy feature reuse, low latency serving, time travel, and ensuring consistency between features used in training and inference workflows. A feature group is a logical grouping of ML features whose organization and structure is defined by a feature group schema. A feature group schema consists of a list of feature definitions, each of which specifies the name, type, and metadata of a feature. The metadata can include information such as data sensitivity, authorship, description, and parameters. The metadata can help make features discoverable, understandable, and traceable.Amazon SageMaker Feature Store allows users to set feature groups for the current features that the ML models use, and assign the required metadata for each feature using the AWS SDK for Python (Boto3), AWS Command Line Interface (AWS CLI), or Amazon SageMaker Studio1.

Amazon QuickSight is a fully managed, serverless business intelligence service that makes it easy to create and publish interactive dashboards that include ML insights. Amazon QuickSight can connect to various data sources, such as Amazon S3, Amazon Athena, Amazon Redshift, and Amazon SageMaker Feature Store, and analyze the data using standard SQL or built-in ML-powered analytics. Amazon QuickSight can also create rich visualizations and reports that can be accessed from any device, and securely shared with anyone inside or outside an organization.Amazon QuickSight can be used to analyze the metadata of the features stored in Amazon SageMaker Feature Store, and generate a report that summarizes the metadata analysis2.

The other options are either more complex or less effective than the proposed solution. Using Amazon SageMaker Data Wrangler to select the features and create a data flow to perform feature-level metadata analysis would require additional steps and resources, and may not capture all the metadata attributes that the company requires. Creating an Amazon DynamoDB table to store feature-level metadata would introduce redundancy and inconsistency, as the metadata is already stored in Amazon SageMaker Feature Store. Using SageMaker Studio to analyze the metadata would not generate a report that can be easily shared and accessed by the company.

1: Amazon SageMaker Feature Store -- Amazon Web Services

2: Amazon QuickSight -- Business Intelligence Service - Amazon Web Services

by Tamar at Dec 27, 2023, 04:03 PM

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26 days ago

Hmm, this is an interesting question. It really comes down to which solution provides the most efficient and streamlined approach to meeting the requirements. I'm leaning towards option B - using SageMaker Feature Store to set feature groups and assign the required metadata seems like the most straightforward approach.

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