MLA-C01 Practice Question: Which TWO data storage options are commonly used…

Which TWO data storage options are commonly used by Amazon SageMaker Feature Store for offline and online storage?

⚠ Common exam trap

Candidates often confuse Amazon ElastiCache (a caching layer) with the primary online storage service, or assume Amazon Redshift is used for offline storage due to its analytical capabilities, but SageMaker Feature Store specifically integrates DynamoDB for online and S3 for offline storage as first-class options.

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 S3

Amazon S3 (option D) is correct because SageMaker Feature Store uses it as the underlying offline store, where feature data is written in Parquet format for historical/batch retrieval and training. Amazon DynamoDB (option E) is correct because it serves as the online store, providing low-latency, high-throughput reads of the latest feature values for real-time inference. Amazon Redshift (A), Amazon RDS (B), and Amazon ElastiCache (C) are not the managed storage backends used by Feature Store for its online and offline stores, even though they are valid AWS data services in other contexts.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

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Amazon Redshift
Why it's wrong here
SageMaker Feature Store uses Amazon S3 for its offline store and a low-latency online store; Redshift is a data warehouse, not a supported backing store for either. It is tempting because Redshift handles analytical feature queries well, and would be correct if the requirement were a separate warehouse for batch feature analysis.
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Amazon RDS
Why it's wrong here
SageMaker Feature Store's offline store is Amazon S3 and its online store is a purpose-built low-latency store; RDS is not a supported option for either. It is tempting because RDS provides durable relational storage, and would be correct for transactional application data rather than feature group storage.
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Amazon ElastiCache
Why it's wrong here
ElastiCache is not a supported backing store for SageMaker Feature Store, which uses Amazon S3 offline and a managed online store. It is tempting because ElastiCache offers the low-latency reads online serving needs, and would be correct for caching feature vectors in a custom inference pipeline.
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Amazon S3
Why this is correct
Amazon S3 serves as the offline store, holding historical feature data in Parquet for training and batch retrieval, while the online store provides low-latency serving. S3 satisfies the offline storage requirement of SageMaker Feature Store.
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Amazon DynamoDB
Why this is correct
DynamoDB provides the low-latency key-value lookups required for the online feature store, serving features in single-digit milliseconds for real-time inference. SageMaker Feature Store pairs it with Amazon S3 for the offline store, so DynamoDB satisfies the online serving constraint.

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

MLA-C01 Practice Question: Which TWO data storage options are commonly used…

Why each option matters

Option-by-option breakdown

AWS S3 Storage Class Comparison

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