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.
Option C is correct because storing the data in Apache Parquet, a columnar format, allows Athena to read only the columns referenced in a query and leverages columnar compression and predicate pushdown, which dramatically reduces the amount of data scanned and therefore query cost and latency. Option D is correct because partitioning the data by date and sensor ID lets Athena prune irrelevant partitions using the partition metadata in the AWS Glue Data Catalog, so queries only scan the specific date/sensor combinations needed instead of the entire dataset. Together, Parquet plus partitioning is the standard best practice for cost-efficient, high-performance Athena queries over S3 data lakes. Option A is not correct because GZIP is a row-based compression format that cannot be split for parallel reads and does not provide column pruning, so it is inferior to Parquet's built-in columnar compression for this use case. Option B is not correct because S3 Standard-IA is a storage-class cost optimization for infrequently accessed data, not a query-performance optimization, and it can even add retrieval costs for frequently queried near real-time data. Option E is not correct because Requester Pays shifts data-transfer costs to the requester and does nothing to improve Athena query performance or reduce the team's own query scanning costs.
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, so Athena reads only the columns referenced in each query and scans far less data than row-based CSV or JSON. This directly satisfies the stem's requirement to optimise query performance and minimise cost, since Athena charges per terabyte scanned.
- ✓
Partition the data by date and sensor ID.
Why this is correct
Partitioning by date and sensor ID lets Athena prune irrelevant partitions via predicate pushdown, scanning only matching prefixes rather than the whole dataset. This satisfies the stem's requirement to optimise query performance and minimise cost, because Athena bills per byte scanned.
- ✗
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
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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.