AWS Certified Cloud Practitioner

Explanation

SageMaker Feature Store is the correct answer because it is specifically designed as a purpose-built repository for storing, sharing, and managing ML features across multiple models and teams.

Key Features of SageMaker Feature Store:

1. Centralized Feature Repository: Provides a single place to store, discover, and share features across ML models

2. Feature Reusability: Allows features to be created once and reused across multiple ML models

3. Feature Consistency: Ensures consistent feature values during training and inference

4. Feature Discovery: Enables data scientists to discover and reuse existing features

5. Online and Offline Stores: Supports both low-latency online serving and batch processing

Why Other Options Are Incorrect:

• SageMaker Ground Truth (B): This is Amazon's data labeling service for creating high-quality training datasets, not for feature storage and reuse.

• SageMaker Canvas (C): This is a visual, no-code interface for building ML models, primarily designed for business analysts.

• SageMaker JumpStart (D): This provides pre-built solutions, pre-trained models, and example notebooks to help users get started quickly with ML.

Use Cases for Feature Store:

• Feature Sharing: Teams can share engineered features instead of recreating them

• Model Consistency: Ensures training and inference use the same feature values

• Feature Lineage: Tracks the origin and transformations of features

• Real-time Features: Supports both batch and real-time feature serving

This feature is particularly valuable in enterprise ML environments where multiple teams work on different models but can benefit from shared feature engineering efforts.