AWS Certified AI Practitioner

Explanation

SageMaker Feature Store is the correct answer because it is specifically designed to store, share, and manage features for machine learning workflows.

Key Points:

1. Purpose of SageMaker Feature Store:

   • Provides a centralized repository for storing and serving features
   • Enables feature reuse across multiple ML models
   • Maintains feature consistency and reduces duplication of effort
   • Supports both online (low-latency) and offline (batch) feature serving

2. 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 (IDE) for ML, not specifically for feature storage
   • D) SageMaker JumpStart: A hub for pre-built solutions and models, not designed for feature storage

3. Use Case Context:

   • Engineered features like "credit utilization ratio" are calculated once and can be used by multiple models
   • Feature Store ensures consistency - all models use the same feature values
   • Reduces computational overhead by avoiding redundant feature calculations

4. Benefits for Financial Analytics:

   • Ensures regulatory compliance through consistent feature definitions
   • Accelerates model development by reusing existing features
   • Maintains data lineage and versioning for audit purposes

This solution aligns with AWS's best practices for building scalable, maintainable ML pipelines where features are treated as first-class citizens.