AWS Certified Cloud Practitioner

Explanation

SageMaker Feature Store is specifically designed for storing, sharing, and managing ML features across multiple models and teams. It provides a centralized repository for feature data that can be reused across different ML workflows.

Why other options are incorrect:

• B) SageMaker JumpStart: This is a feature that provides pre-built solutions, model templates, and example notebooks to help users get started quickly with ML projects.

• C) SageMaker Ground Truth: This service is used for data labeling and annotation, helping to create high-quality training datasets.

• D) SageMaker Canvas: This is a visual, no-code interface for building ML models, designed for business analysts and non-technical users.

Key Benefits of SageMaker Feature Store:

• Feature Reusability: Store features once and use them across multiple models

• Feature Consistency: Ensure consistent feature values during training and inference

• Feature Discovery: Easily discover and share features across teams

• Online/Offline Storage: Supports both low-latency online access for real-time inference and batch access for training