AWS Certified Cloud Practitioner

Amazon SageMaker Feature Store is the correct answer because it is specifically designed to store, share, and manage features (variables) for machine learning models across multiple teams.

Key reasons why Feature Store meets the requirements:

1. Feature Sharing: Allows multiple teams to access and reuse the same features, promoting consistency and collaboration
2. Feature Management: Provides versioning, discovery, and governance capabilities for features
3. Centralized Repository: Creates a single source of truth for features used across different models and projects
4. Real-time and Batch Features: Supports both online (real-time) and offline (batch) feature serving

Why other options are incorrect:

• Amazon SageMaker Data Wrangler: Used for data preparation and feature engineering, but not specifically for sharing and managing features across teams
• Amazon SageMaker Clarify: Used for model explainability and bias detection, not for feature management
• Amazon SageMaker Model Cards: Used for documenting model information and metadata, not for feature management

Use Case: When multiple teams are working on different ML models that use the same features, Feature Store ensures consistency, reduces redundant work, and maintains feature lineage.