MLA-C01 Data Preparation for Machine Learning Practice Question

A data engineer is preparing a dataset for a SageMaker training job. The dataset contains a timestamp column and is stored in Amazon S3 as CSV files. The engineer needs to ensure that the training job reads the data efficiently and that the data is partitioned by date to improve query performance in Amazon Athena. Which action should the engineer take?

⚠ Common exam trap

The trap here is thinking that simply cataloging CSV data or partitioning without converting format is sufficient for both SageMaker efficiency and Athena performance.

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

✓

Convert the CSV files to Parquet format and partition them by date in S3 using a Hive-style partition scheme (e.g., year=2023/month=01/day=01/).

Converting CSV to Parquet reduces storage size and enables columnar reads, which speeds up SageMaker training jobs. Partitioning by date with Hive-style prefixes allows Athena to prune partitions, reducing query cost and latency. Together, these actions satisfy both the training efficiency and Athena query performance requirements.

Answer analysis

Option-by-option breakdown

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

✗
Convert the CSV files to Parquet format and store them in a single prefix without partitioning.
Why it's wrong here
Parquet improves read efficiency for SageMaker, but without partitioning, Athena must scan the entire dataset for date-filtered queries, leading to higher cost and slower performance. The requirement explicitly includes partitioning by date to improve Athena query performance, so this fails that part.
✓
Convert the CSV files to Parquet format and partition them by date in S3 using a Hive-style partition scheme (e.g., year=2023/month=01/day=01/).
Why this is correct
Converting to Parquet improves compression and columnar read performance, which benefits SageMaker training jobs. Partitioning by date using Hive-style prefixes allows Athena to prune partitions and reduce query scan, improving performance. This meets both efficiency and query performance requirements.
✗
Keep the CSV format but partition the files by date using a flat prefix structure (e.g., 2023-01-01/).
Why it's wrong here
While partitioning can help Athena, CSV is not columnar and lacks efficient compression, so SageMaker training jobs will read more data than necessary. Flat prefixes without key-value pairs are not recognized by Athena as Hive partitions, so partition pruning may not work optimally.
✗
Use AWS Glue to crawl the CSV files and create a table in the AWS Glue Data Catalog, then use Athena to query the data directly without converting or partitioning.
Why it's wrong here
Crawling and cataloging enables Athena queries, but CSV format and lack of partitioning mean poor performance for both Athena and SageMaker. The data engineer needs to optimize storage format and layout, not just catalog it. This does not meet the efficiency and partitioning requirements.

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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