DEA-C01 Data Store Management Practice Question
A data engineering team is designing a data lake on Amazon S3 for storing sensor data from IoT devices. The data is written in near real-time and needs to be queried using Amazon Athena. Which TWO configurations should the team implement to optimize query performance and minimize costs?
⚠ Common exam trap
AWS often tests the misconception that any compression (like GZIP alone) is sufficient for Athena optimization, but the trap is that without a columnar format like Parquet or ORC, compression alone does not enable column pruning or predicate pushdown, leading to higher scan costs and slower queries.
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
✓
Store the data in Apache Parquet format.
Apache Parquet is a columnar storage format that allows Athena to read only the columns needed for a query, drastically reducing I/O and scan costs. Combined with compression (like Snappy or GZIP), Parquet minimizes the amount of data scanned per query, which directly lowers Athena's cost (charged per TB scanned) and improves query performance through predicate pushdown and efficient encoding.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Compress the data using GZIP.
Why it's wrong here
GZIP is not splittable and less efficient for Athena.
- ✗
Use S3 Standard-IA storage class.
Why it's wrong here
Standard-IA incurs retrieval costs and minimum charges.
- ✓
Store the data in Apache Parquet format.
Why this is correct
Parquet is columnar and reduces scan size.
- ✓
Partition the data by date and sensor ID.
Why this is correct
Partitioning reduces data scanned by Athena.
- ✗
Enable Requester Pays on the S3 bucket.
Why it's wrong here
Requester Pays does not optimize performance.
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
1 more way 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 engineering team is designing a data lake on Amazon S3. They need to store raw data in a format that supports schema evolution and is optimized for analytics with Amazon Athena. Which storage format should they use?
hard- ✓ A.Parquet
- B.CSV
- C.Avro
- D.JSON
Why A: Parquet is the correct choice because it is a columnar storage format that is highly optimized for Amazon Athena, which can read only the necessary columns to minimize data scanned and reduce query costs. Parquet also supports schema evolution through features like nested data structures and the ability to add or remove columns without rewriting the entire dataset, making it ideal for raw data lakes that must accommodate changing schemas over time.
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.