- A
Amazon S3 with AWS Glue Data Catalog
Why wrong: S3 can store features, but it lacks the low-latency online serving capability and feature management features.
- B
Amazon Redshift
Why wrong: Redshift is a data warehouse for analytics, not designed for low-latency feature serving for real-time ML inference.
- C
Amazon SageMaker Feature Store
Feature Store provides both online and offline stores, plus features like point-in-time queries and feature group management.
- D
Amazon DynamoDB
Why wrong: DynamoDB is a NoSQL database that can serve features in real-time, but it lacks the offline store for batch training and feature management capabilities.
Question 161 of 1,000
easyMultiple ChoiceObjective-mapped
Amazon SageMaker Feature Store
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. 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 machine learning engineer wants to store, share, and manage features for multiple ML models across an organization. The features need to be accessible for both real-time inference (low-latency) and batch training. Which AWS service should the engineer use?
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
✓
Amazon SageMaker Feature Store
Amazon SageMaker Feature Store is purpose-built for storing, sharing, and managing ML features across teams and models. It provides a unified feature store with both an online store (backed by Amazon DynamoDB or Redis) for low-latency real-time inference and an offline store (backed by Amazon S3) for batch training, directly addressing the requirement for dual access patterns.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 S3 with AWS Glue Data Catalog
Why it's wrong here
S3 can store features, but it lacks the low-latency online serving capability and feature management features.
- ✗
Amazon Redshift
Why it's wrong here
Redshift is a data warehouse for analytics, not designed for low-latency feature serving for real-time ML inference.
- ✓
Amazon SageMaker Feature Store
Why this is correct
Feature Store provides both online and offline stores, plus features like point-in-time queries and feature group management.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Amazon DynamoDB
Why it's wrong here
DynamoDB is a NoSQL database that can serve features in real-time, but it lacks the offline store for batch training and feature management capabilities.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse a general-purpose database or data lake (like DynamoDB or S3) with a purpose-built ML feature store, overlooking the need for both low-latency online access and offline batch storage with feature-specific governance.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker Feature Store uses an online store (DynamoDB or Redis) for real-time serving with single-digit millisecond latency and an offline store (S3 with Parquet format) for historical feature data used in training. It enforces a feature group schema with a record identifier and event time, enabling point-in-time queries to avoid data leakage during training. A real-world scenario is a fraud detection system where features like transaction velocity must be retrieved in real-time for inference and also stored historically for model retraining, all managed through a single feature group.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
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.
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 |
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Amazon SageMaker Feature Store — Amazon SageMaker Feature Store is purpose-built for storing, sharing, and managing ML features across teams and models. It provides a unified feature store with both an online store (backed by Amazon DynamoDB or Redis) for low-latency real-time inference and an offline store (backed by Amazon S3) for batch training, directly addressing the requirement for dual access patterns.
What should I do if I get this MLA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 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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