MLA-C01 Data Preparation for Machine Learning Practice Question

A company has a dataset of 2 billion records stored as text files in Amazon S3. The data is partitioned by year and month. The data science team wants to read only the last 6 months of data for model training using SageMaker. To minimize data scanned and reduce costs, which approach should the team use?

⚠ Common exam trap

AWS often tests the misconception that SageMaker's Input Mode 'File' or S3 Select can efficiently filter partitioned data, but the key trap is that partition pruning requires a catalog service (like Glue) and a query engine (like Athena) to avoid scanning all objects or listing the entire bucket.

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

✓

Use AWS Glue to create a catalog table with partitions, then query with Athena to create a filtered dataset in S3.

AWS Glue can crawl the S3 data to create a catalog table with partitions by year and month. Athena can then query only the partitions corresponding to the last 6 months, scanning minimal data and writing the filtered results back to S3 for SageMaker training. This approach leverages partition pruning to reduce costs and avoids loading or processing the full 2 billion records.

Answer analysis

Option-by-option breakdown

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

✗
Use S3 Select to retrieve only the last 6 months of data by applying an SQL expression on each object.
Why it's wrong here
S3 Select works per object and does not support partition filtering across directories.
✓
Use AWS Glue to create a catalog table with partitions, then query with Athena to create a filtered dataset in S3.
Why this is correct
Partition pruning ensures only relevant data is scanned.
✗
Use SageMaker Processing with a script that lists all objects in the bucket and reads only those with the desired prefixes.
Why it's wrong here
This still lists all objects and may incur listing costs.
✗
Use SageMaker Processing with Input Mode 'File' and specify the S3 prefix for the last 6 months.
Why it's wrong here
This still downloads all listed files; without partition pruning, it scans all files.

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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