Quick Answer
The answer is Amazon SageMaker Feature Store. This service is the correct choice because it provides a centralized repository for storing, managing, and serving ML features with both an online store for low-latency inference and an offline store for batch training, directly supporting the team’s need for real-time recommendations and feature reuse across multiple models. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your understanding of feature engineering infrastructure—specifically how to decouple feature storage from model training and inference. A common trap is choosing DynamoDB for low-latency access or S3 alone for offline storage, but SageMaker Feature Store uniquely unifies both stores with consistent feature definitions and point-in-time retrieval. Memory tip: think “Feature Store = one registry for online speed + offline scale,” and remember that if the question mentions reusing features across models, the answer is almost always Feature Store.
This MLA-C01 practice question tests your understanding of data preparation for machine learning. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Amazon DynamoDB with TTL.
Why wrong: DynamoDB can store features but lacks SageMaker integration and feature management.
AWS Glue Data Catalog.
Why wrong: Data Catalog stores table metadata, not feature values.
SageMaker Feature Store.
Feature Store provides online and offline feature storage with low latency.
Amazon S3 with AWS Lambda for serving.
Why wrong: S3 has high latency for real-time serving and no built-in feature management.
Answer choices
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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.
Related concept
Read the scenario before looking for a memorised answer.
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.
Common exam traps
The trap here is that candidates often 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.
Detailed technical explanation
SageMaker Feature Store uses a dual-storage architecture: an online store backed by Amazon DynamoDB (or a low-latency in-memory cache) for millisecond-level retrieval, and an offline store backed by Amazon S3 for batch training. It enforces a feature group schema with a record identifier and event time, enabling point-in-time correct joins for training datasets, which is critical for avoiding data leakage in time-series models like recommendation systems.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
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FAQ
Data Preparation for Machine Learning — This question tests Data Preparation for Machine Learning — Read the scenario before looking for a memorised answer..
The correct answer is: 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
This MLA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLA-C01 exam.
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