AWS Certified Machine Learning – SpecialtyMachine Learning Implementation and OperationsEasy
A data science team is developing a new credit risk assessment model. They need a centralized, versioned repository to store, manage, and share features across different machine learning models and teams. This repository must support both online inference and offline training. Which AWS service is best suited for this requirement?
- AAmazon RDS
- BAmazon SageMaker Feature Store
- CAmazon DynamoDB
- DAmazon S3
Show answer & explanationAnswer & explanation
Correct answer: B. Amazon SageMaker Feature Store
Amazon SageMaker Feature Store is a purpose-built service for machine learning that provides a centralized repository for features. It supports both online (low-latency) and offline (batch) access for training and inference, and includes capabilities for feature versioning and sharing, directly meeting all stated requirements.
Why the other options are wrong
- A. Amazon RDS is a relational database service, not optimized as a comprehensive feature store for ML workflows.
- C. Amazon DynamoDB is a NoSQL database, which could store features but lacks the integrated ML-specific functionalities like offline store, versioning, and direct integration with SageMaker for feature engineering.
- D. Amazon S3 is an object storage service, not a purpose-built feature store with online/offline access and versioning capabilities for ML.
SageMaker Feature Store
A fully managed, purpose-built repository for machine learning features that simplifies the process of storing, discovering, and sharing features for training and inference.
- Supports online store for low-latency inference.
- Supports offline store for batch training and analysis.
- Enables feature versioning and reusability across models/teams.
Memory trick: Features stored, ready to serve.