DEA-C01 Data Store Management Practice Question

A data engineer is designing a data lake on Amazon S3. The team wants to optimize query performance and reduce storage costs for a large dataset of JSON logs that are queried frequently by Amazon Athena. The logs are currently stored as uncompressed JSON files, each around 1 GB, in a single prefix. The engineer needs to improve query performance and reduce costs without changing the data format. Which action should the engineer take?

⚠ Common exam trap

The trap here is assuming that converting to a columnar format like Parquet is always the best optimization, but the scenario explicitly requires keeping the data in its original JSON format.

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

✓

Compress the JSON files with gzip and split them into smaller files of approximately 128 MB.

Compressing the JSON files with gzip reduces the storage footprint and the amount of data scanned by Athena, lowering costs. Splitting the large files into smaller, more manageable sizes allows Athena to process them in parallel, significantly improving query performance. This solution respects the requirement to keep the data in JSON format and directly addresses both performance and cost concerns.

Answer analysis

Option-by-option breakdown

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

✓
Compress the JSON files with gzip and split them into smaller files of approximately 128 MB.
Why this is correct
Compressing with gzip reduces storage costs and the amount of data scanned by Athena. Splitting large files into smaller ones (around 128 MB) enables parallel processing and improves query performance. This approach maintains the JSON format and directly addresses the requirements without altering the data structure.
✗
Convert the JSON files to Apache Parquet and partition the data by date.
Why it's wrong here
Converting to Parquet changes the data format, which the scenario explicitly prohibits. While Parquet and partitioning would improve performance and reduce costs, the requirement is to keep the original JSON format. Therefore, this action does not meet the constraint and is not the best choice.
✗
Move the data to Amazon Redshift and query it using Redshift Spectrum.
Why it's wrong here
Moving data to Redshift involves additional cost and complexity, and Redshift Spectrum is used to query data directly from S3. However, the scenario focuses on optimizing the existing S3 data lake for Athena, not migrating to another service. This approach does not address the immediate need to improve Athena performance and reduce S3 storage costs.
✗
Enable S3 Transfer Acceleration on the bucket to speed up data retrieval.
Why it's wrong here
S3 Transfer Acceleration speeds up uploads and downloads over long distances but does not improve Athena query performance or reduce storage costs. It is designed for faster data transfer, not for optimizing analytical queries. Thus, it does not solve the performance and cost issues described.

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

DEA-C01 Data Store Management Practice Question

Why each option matters

Option-by-option breakdown

AWS S3 Storage Class Comparison

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