- A
Store data in uncompressed CSV format and partition by year, month, day, hour.
Why wrong: CSV is not columnar and scanning all columns increases cost.
- B
Use JSON format with Snappy compression and partition by date only.
Why wrong: JSON is verbose and not as efficient as Parquet.
- C
Use Gzip-compressed CSV files with no partitioning.
Why wrong: No partitioning forces full table scans.
- D
Use Parquet format with Snappy compression and partition by year, month, day.
Parquet is columnar, reducing I/O, and partitioning limits data scanned.
Question 811 of 1,786
DEA-C01 Data Store Management Practice Question
This DEA-C01 practice question tests your understanding of data store management. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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.
Option D is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
Parquet stores data column-wise, enabling predicate pushdown and efficient compression (e.g., dictionary encoding for low-cardinality columns). Snappy compression is splittable and has low CPU overhead, making it ideal for Athena’s distributed engine. Partitioning by year, month, and day creates a hierarchical directory structure (e.g., s3://bucket/year=2024/month=12/day=01/) that Athena uses to prune partitions via the WHERE clause, drastically reducing I/O.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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 |
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this DEA-C01 question test?
Data Store Management — This question tests Data Store Management — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use Parquet format with Snappy compression and partition by year, month, day. — Option D is correct because 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.
What should I do if I get this DEA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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Last reviewed: Jul 4, 2026
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.
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