MLA-C01 Data Preparation for Machine Learning Practice Question

A data engineer stores raw ML training data in Amazon S3 and needs to catalog the schema, track partition changes, and make the data queryable by Amazon Athena without running ETL. Which AWS service should the engineer use?

⚠ Common exam trap

Many exam-takers confuse a service that consumes the Glue Data Catalog, such as Athena or Redshift Spectrum, with the service that actually builds and maintains the catalog entries.

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

✓

AWS Glue Data Catalog with an AWS Glue crawler.

The Glue Data Catalog is the central metadata repository that stores table definitions, schemas, and partition information for data in S3, and Athena natively uses it as its metastore. A crawler automates schema inference and partition discovery, so no ETL is required. The other services either transform data, serve features, or consume the catalog rather than create it.

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 SageMaker Feature Store with an offline store backed by S3.
Why it's wrong here
Feature Store is designed to serve and version engineered features for training and inference, not to catalog arbitrary raw files or expose them to Athena SQL. It requires a feature group definition and ingestion step, which is more work than the requirement implies. It does not provide the schema-discovery and partition-tracking behavior the engineer needs.
✗
AWS Glue ETL job with a Python shell script.
Why it's wrong here
An ETL job actively reads, transforms, and writes data, which contradicts the requirement to avoid running ETL. It also does not by itself register a queryable table with partition metadata for Athena unless paired with catalog operations. This option adds unnecessary compute and data movement for a pure cataloging need.
✓
AWS Glue Data Catalog with an AWS Glue crawler.
Why this is correct
A Glue crawler inspects data in S3, infers schema and partitions, and registers tables in the Glue Data Catalog, which Athena uses as its metastore for querying S3 data in place. This satisfies cataloging, partition tracking, and query access without moving or transforming the data, matching the stated requirement exactly.
✗
Amazon Redshift Spectrum with an external schema.
Why it's wrong here
Redshift Spectrum queries S3 data through an external schema, but it still depends on the Glue Data Catalog for table definitions and adds a Redshift cluster or serverless endpoint. For a team that only needs cataloging plus Athena access, this introduces extra cost and infrastructure. It is a downstream consumer of the catalog rather than the cataloging mechanism itself.

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 Data Preparation for Machine Learning Practice Question

Why each option matters

Option-by-option breakdown

AWS S3 Storage Class Comparison

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