DEA-C01 Data Store Management Practice Question
A data engineer is building a data lake on Amazon S3 and must choose the optimal file format for a dataset that is queried by Amazon Athena. The queries typically select a few columns from wide tables containing hundreds of columns, and the data volume is in terabytes. The engineer wants to minimize query scan costs and improve performance. Which file format should the engineer use?
⚠ Common exam trap
The trap here is assuming that any columnar format is equally optimal, but Parquet specifically excels for Athena due to its widespread support and predicate pushdown capabilities.
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
✓
Apache Parquet
Apache Parquet is a columnar storage format that allows Amazon Athena to read only the columns needed for a query, significantly reducing the amount of data scanned. For wide tables where queries access a small subset of columns, this column pruning leads to lower costs and faster performance. Parquet also offers efficient compression, further reducing storage and scan volume, making it the best choice for this scenario.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Apache Avro
Why it's wrong here
Avro is a row-based format that stores entire rows together. When Athena queries a few columns, it must still read complete rows, scanning all columns and increasing data processed. While Avro is useful for write-heavy workloads and schema evolution, it does not provide the column pruning benefits needed to minimize scan costs for wide tables with selective column access.
- ✗
CSV
Why it's wrong here
CSV is a row-based, text format without compression or columnar optimizations. When Athena queries specific columns, it scans all columns in each row, increasing data processed and cost. CSV also lacks schema enforcement and efficient encoding, resulting in larger files and slower queries. It is not suitable for minimizing scan costs on wide tables in a data lake.
- ✓
Apache Parquet
Why this is correct
Parquet is a columnar format that stores data by column, enabling Athena to read only the columns referenced in a query. This drastically reduces the amount of data scanned, lowering costs and improving performance for wide tables where only a few columns are accessed. It also supports efficient compression and encoding, further reducing storage and scan volume, making it ideal for this analytical workload.
- ✗
JSON
Why it's wrong here
JSON is a text-based, row-oriented format that lacks efficient columnar storage. Athena must parse entire documents to extract queried fields, leading to high scan volumes and slower performance. JSON also typically has larger file sizes due to verbose syntax and limited compression efficiency, making it a poor choice for cost-effective analytical queries on large datasets.
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 |
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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.