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 with Amazon Athena. The engineer wants to minimize query costs and improve performance. Which action should be taken?
⚠ Common exam trap
The trap here is focusing only on compression or partitioning while overlooking the benefit of columnar storage for column pruning.
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 data to columnar format like Apache Parquet and partition it.
Using a columnar format like Parquet with partitioning allows Athena to scan only the necessary columns and partitions, drastically reducing the amount of data scanned and thus the cost. This is the recommended best practice for optimizing Athena queries on S3 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.
- ✗
Increase the number of partitions to one per day.
Why it's wrong here
Partitioning by day can improve performance if queries filter by date, but it does not change the underlying row-based JSON format. Without columnar storage, Athena still scans all columns in the selected partitions. Partitioning alone is insufficient to minimize costs for queries that select specific columns.
- ✗
Store the data in a single large JSON file.
Why it's wrong here
Storing data in a single large file reduces parallelism in Athena, as the query engine reads the entire file even if only a few columns are needed. This increases query runtime and cost because Athena charges based on data scanned. For better performance and lower cost, data should be split into multiple files and compressed.
- ✗
Use gzip compression on each JSON file.
Why it's wrong here
Compressing JSON files with gzip reduces storage size and data scanned, which can lower costs. However, JSON is row-based, so Athena must still read all columns even if only a few are needed. Columnar formats like Parquet provide better compression and enable column pruning, leading to greater cost savings.
- ✓
Convert the data to columnar format like Apache Parquet and partition it.
Why this is correct
Converting to a columnar format such as Parquet allows Athena to read only the columns referenced in the query, reducing data scanned and thus cost. Partitioning further limits the amount of data scanned by filtering on partition keys. This combination significantly improves performance and reduces query costs.
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
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
This DEA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DEA-C01 exam.