- A
Amazon DynamoDB
Why wrong: DynamoDB is a NoSQL database, not a feature store with offline store and point-in-time queries.
- B
Amazon S3
Why wrong: S3 provides storage but no feature management, sharing, or online serving.
- C
AWS Glue Data Catalog
Why wrong: Glue Data Catalog is a metadata repository for tables, not feature management.
- D
Amazon SageMaker Feature Store
Designed specifically for feature storage, sharing, and serving.
Question 223 of 1,000
easyMultiple ChoiceObjective-mapped
Amazon SageMaker Feature Store — Track, Share, and Serve Features
This MLA-C01 practice question tests your understanding of a data scientist wants to track feature…. 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 data scientist wants to track feature definitions, share them across teams, and serve features for both training and real-time inference. Which AWS service provides these capabilities?
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 ML workflows, providing a centralized repository to define, share, and serve features for both training (batch) and real-time inference (low-latency retrieval). It supports offline and online stores, enabling consistent feature definitions across teams and automatic feature ingestion via SageMaker Pipelines or custom code.
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 DynamoDB
Why it's wrong here
DynamoDB is a NoSQL database, not a feature store with offline store and point-in-time queries.
- ✗
Amazon S3
Why it's wrong here
S3 provides storage but no feature management, sharing, or online serving.
- ✗
AWS Glue Data Catalog
Why it's wrong here
Glue Data Catalog is a metadata repository for tables, not feature management.
- ✓
Amazon SageMaker Feature Store
Why this is correct
Designed specifically for feature storage, sharing, and serving.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse a general-purpose storage or catalog service (like S3 or Glue Data Catalog) with a purpose-built ML feature store, overlooking the need for both offline and online serving with feature-specific management.
Detailed technical explanation
How to think about this question
SageMaker Feature Store uses an offline store (backed by S3 and a metadata database) for batch training and an online store (backed by Amazon DynamoDB or Redis) for real-time inference, with automatic synchronization between them. It supports feature groups with record identifiers and event times, enabling point-in-time queries to avoid data leakage. In practice, teams can define features once and reuse them across multiple models, ensuring consistency and reducing duplication.
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.
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 ML workflows, providing a centralized repository to define, share, and serve features for both training (batch) and real-time inference (low-latency retrieval). It supports offline and online stores, enabling consistent feature definitions across teams and automatic feature ingestion via SageMaker Pipelines or custom code.
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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