AWS Certified Cloud Practitioner

Explanation

SageMaker Feature Store is specifically designed for storing, sharing, and managing ML features across multiple models and teams. Here's why it's the correct choice:

Key Features of SageMaker Feature Store:

• Feature Storage: Provides a centralized repository for storing engineered features

• Feature Reuse: Allows features to be shared and reused across different ML models

• Feature Discovery: Enables teams to discover and use features created by others

• Feature Consistency: Ensures consistent feature values across training and inference

• Feature Lineage: Tracks the origin and transformations of features

Why Other Options Are Incorrect:

• A) SageMaker Ground Truth: Used for data labeling and annotation, not feature storage

• C) SageMaker Studio: An integrated development environment for ML workflows

• D) SageMaker JumpStart: Provides pre-built solutions and model templates

Use Case Fit:

For storing engineered features like "credit utilization ratio" that need to be reused across multiple ML models, SageMaker Feature Store is the perfect solution as it's specifically built for feature management and sharing in ML workflows.