DEA-C01 Data Store Management Practice Question
A data engineer is designing a data lake on Amazon S3. The data is ingested from multiple sources in Parquet format, partitioned by date. The engineer needs to ensure that queries using Amazon Athena are cost-effective and perform well. Which approach should the engineer take?
⚠ Common exam trap
The DEA-C01 exam often tests the misconception that any compression or any partitioning is sufficient, but the trap here is that row-based formats (CSV, JSON) and non-hierarchical partitioning fail to optimize Athena’s columnar scan and partition pruning capabilities, leading to higher costs and slower 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
✓
Use Parquet format with Snappy compression and partition by year, month, day.
Parquet is a columnar storage format that reduces the amount of data scanned by Athena, and Snappy compression provides a good balance between compression ratio and decompression speed. Partitioning by year, month, and day allows Athena to use partition pruning to skip irrelevant data, minimizing scanned bytes and reducing query cost.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Store data in uncompressed CSV format and partition by year, month, day, hour.
Why it's wrong here
CSV is not columnar and scanning all columns increases cost.
- ✗
Use JSON format with Snappy compression and partition by date only.
Why it's wrong here
JSON is verbose and not as efficient as Parquet.
- ✗
Use Gzip-compressed CSV files with no partitioning.
Why it's wrong here
No partitioning forces full table scans.
- ✓
Use Parquet format with Snappy compression and partition by year, month, day.
Why this is correct
Parquet is columnar, reducing I/O, and partitioning limits data scanned.
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
Related to this question
About these practice questions
One of 1,711 original DEA-C01 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
2 more ways this is tested on DEA-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data engineer is designing a data lake on Amazon S3. Data is ingested from multiple sources in JSON format. The engineer needs to optimize query performance for Amazon Athena while minimizing storage costs. Which storage strategy should the engineer use?
medium- A.Store data as CSV files in a single S3 bucket without prefixes.
- ✓ B.Convert data to Parquet format and partition by date.
- C.Store data as JSON files in a single prefix without partitioning.
- D.Store compressed JSON files in Amazon S3 Glacier.
Why B: Parquet is a columnar storage format that significantly reduces data scan volume in Amazon Athena, which charges per byte scanned. Partitioning by date further limits the data scanned to only relevant partitions, optimizing both query performance and cost. JSON and CSV are row-based formats that require full scans, and Glacier is unsuitable for interactive querying.
Variation 2. A data engineer is designing a data lake on Amazon S3. The data is partitioned by year, month, day, and hour. The engineer needs to ensure that queries using Amazon Athena are cost-effective and performant. The data is written in Parquet format, and the total volume is 50 TB. Which approach minimizes query costs?
hard- A.Use AWS Glue Data Catalog to catalog the data
- B.Convert data to CSV format
- ✓ C.Partition the data by year, month, day, and hour
- D.Use S3 Intelligent-Tiering storage class
Why C: Partitioning by year, month, day, and hour allows Athena to use partition pruning, reading only the relevant S3 prefixes instead of scanning the entire 50 TB dataset. This drastically reduces the amount of data scanned per query, which directly lowers query costs (Athena charges per TB scanned). The existing Parquet format further optimizes performance through columnar storage and compression.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.