DEA-C01 Data Store Management Practice Question
A data engineer is building a data lake on Amazon S3. The raw data arrives as JSON files, but the analytics team needs to query the data using standard SQL in Amazon Athena with optimal performance and minimal cost. The engineer wants to convert the JSON to a columnar format that supports predicate pushdown and is natively supported by Athena. Which storage format should the engineer choose?
⚠ Common exam trap
The trap here is assuming that any format supported by Athena is equally performant, but columnar formats like Parquet provide significant advantages for query performance and cost.
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 the best choice because it is a columnar format that allows Athena to read only the necessary columns, reducing data scanned and cost. It also supports predicate pushdown, which filters data at the storage level. Other formats like Avro, JSON, and CSV are row-based and less efficient for analytical queries in Athena.
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 Parquet
Why this is correct
Apache Parquet is a columnar format that stores data by column, enabling Athena to read only the columns referenced in a query. This reduces I/O and cost. Parquet also supports predicate pushdown and is natively supported by Athena, making it ideal for this scenario where the goal is optimal query performance and minimal cost.
- ✗
CSV
Why it's wrong here
CSV is a row-based, text format that lacks compression and columnar storage. While Athena can query CSV, it does not support predicate pushdown effectively, and queries scan all columns, increasing cost and reducing performance. CSV is not the optimal choice for analytical workloads in Athena.
- ✗
JSON
Why it's wrong here
JSON is a text-based, row-oriented format that is not optimized for analytical queries. Athena can query JSON, but it requires scanning the entire dataset and parsing each record, leading to higher costs and slower performance. The engineer's goal is to convert away from JSON, so retaining JSON does not meet the requirement.
- ✗
Apache Avro
Why it's wrong here
Apache Avro is a row-based format that is efficient for write-heavy workloads and schema evolution, but it does not provide the columnar storage benefits that Athena leverages for fast, cost-effective queries. Using Avro would result in scanning entire rows, increasing data scanned and query cost compared to Parquet.
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.