DEA-C01 Data Store Management Practice Question

A data engineer needs to store large volumes of semi-structured JSON data in Amazon S3 and query it using Amazon Athena. The data is generated continuously and appended to S3 in small files. The engineer wants to optimize query performance and reduce costs. Which action should the engineer take?

⚠ Common exam trap

The trap here is focusing on file size or upload speed instead of the format and partitioning strategy that directly impact Athena query cost and 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 JSON data to Apache Parquet and partition it by date.

Converting JSON to Parquet and partitioning by date leverages columnar storage and partition pruning, which are key optimizations for Athena. Parquet reduces the amount of data scanned by reading only required columns, while partitioning allows Athena to skip irrelevant partitions based on query filters. Together, they minimize cost and improve performance for large-scale semi-structured data.

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 JSON data to Apache Parquet and partition it by date.
Why this is correct
Converting JSON to Parquet provides columnar storage, enabling Athena to read only needed columns and reduce scan volume. Partitioning by date allows Athena to prune partitions based on query filters, further reducing data scanned. This combination significantly improves query performance and lowers costs, making it the optimal solution for large-scale semi-structured data queried by Athena.
✗
Enable S3 Transfer Acceleration for faster data uploads.
Why it's wrong here
S3 Transfer Acceleration speeds up uploads to S3 by using AWS edge locations, but it does not affect query performance or cost in Athena. The bottleneck is query execution and data scanned, not upload speed. This action addresses data ingestion latency, not analytical query efficiency, so it is irrelevant to the stated goal.
✗
Use Amazon S3 Select to query the JSON data directly.
Why it's wrong here
Amazon S3 Select allows simple SQL queries on individual objects, but it does not integrate with Athena's distributed query engine or provide partition pruning across many objects. It is limited to single-object queries and is not suitable for large-scale analytical queries across a data lake. Therefore, it does not meet the requirement for optimizing Athena performance and cost.
✗
Store the JSON data in a single large file per day.
Why it's wrong here
While consolidating small files into larger files improves read efficiency, storing as JSON still requires Athena to scan entire documents and parse all fields. This does not provide columnar benefits or partition pruning. Query performance and costs may improve slightly compared to many small files, but it is not as effective as converting to a columnar format and partitioning.

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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