MLA-C01 Data Preparation for Machine Learning Practice Question
A team is building a recommendation system and wants to store and serve features for online and offline models. The features include user statistics (updated daily) and movie metadata (static). The team needs low-latency inference for real-time recommendations and wants to reuse features across multiple models. Which AWS service should the team use to store, manage, and serve these features?
⚠ Common exam trap
Many candidates confuse a general-purpose database (DynamoDB) or a data catalog (Glue) with a purpose-built ML feature store, overlooking the need for feature-specific capabilities like online/offline consistency, feature versioning, and reuse across models.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
SageMaker Feature Store.
Amazon SageMaker Feature Store is purpose-built for storing, managing, and serving ML features with low-latency retrieval for online inference and batch serving for offline training. It supports feature reuse across multiple models by providing a centralized feature registry, consistent feature definitions, and both online (low-latency) and offline (S3-based) stores, which directly matches the team's requirements for real-time recommendations and cross-model reuse.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Amazon DynamoDB with TTL.
Why it's wrong here
DynamoDB can store features but lacks SageMaker integration and feature management.
- ✗
AWS Glue Data Catalog.
Why it's wrong here
Data Catalog stores table metadata, not feature values.
- ✓
SageMaker Feature Store.
Why this is correct
Feature Store provides online and offline feature storage with low latency.
- ✗
Amazon S3 with AWS Lambda for serving.
Why it's wrong here
S3 has high latency for real-time serving and no built-in feature management.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
Go deeper
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