MLA-C01 Data Preparation for Machine Learning Practice Question

A data engineer needs to prepare a large dataset (10 TB) stored in Amazon S3 for a training job on SageMaker. The data is in CSV format, but the training algorithm expects Parquet for performance. The engineer must transform the data with minimal cost and without writing custom code. Which service should be used?

⚠ Common exam trap

Test-takers frequently confuse Amazon S3 Select's ability to filter data with the ability to transform data formats, but S3 Select only returns filtered results in the original format and cannot perform format conversion like CSV to Parquet.

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 crawler and ETL job that converts CSV to Parquet.

AWS Glue is the correct choice because it provides a serverless, pay-per-use ETL service that can automatically convert CSV to Parquet without writing custom code. The Glue crawler infers the schema, and the ETL job uses built-in transforms to efficiently handle 10 TB of data with minimal cost, as it only charges for the resources consumed during the job execution.

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 AWS Glue to create a crawler and ETL job that converts CSV to Parquet.
Why this is correct
AWS Glue's crawler infers the CSV schema and its serverless Spark ETL writes Parquet directly to S3, satisfying the no-custom-code and minimal-cost constraints for the 10 TB conversion. Unlike Lambda, which caps runtime and memory, Glue scales to terabyte workloads without cluster management.
✗
Use SageMaker Processing with a TensorFlow script to read CSV and write Parquet.
Why it's wrong here
SageMaker Processing runs a user-supplied TensorFlow script, which is custom code, violating the no-code constraint and adding instance-hour costs. It is tempting because Processing jobs are the standard mechanism for bespoke preprocessing that no managed service covers.
✗
Use Amazon S3 Select to convert the data to Parquet during retrieval.
Why it's wrong here
S3 Select filters rows and columns from CSV or JSON during retrieval; it cannot write Parquet output at all, so the format conversion never occurs. It is tempting because it reduces data transferred for selective queries, which is its actual purpose.
✗
Use Amazon EMR with a Spark job to convert the files.
Why it's wrong here
EMR with Spark requires authoring and maintaining transformation code, breaching the no-custom-code requirement, and cluster compute costs exceed a serverless alternative. It is tempting because Spark excels at large-scale format conversion when bespoke logic or complex transforms are genuinely needed.

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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