- A
CSV
Why wrong: CSV is row-oriented and not optimized for analytics.
- B
Parquet
Parquet is columnar, compressed, and ideal for analytics.
- C
ORC
Why wrong: ORC is also columnar but Parquet is more commonly used with EMR.
- D
JSON
Why wrong: JSON is verbose and not columnar.
Question 1,116 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 company is using Amazon EMR to process large datasets stored in Amazon S3. The data engineer wants to reduce the time it takes to read data from S3 by optimizing the data format. Which file format should the engineer recommend?
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
✓
Parquet
Parquet is the correct choice because it is a columnar storage format that significantly reduces the amount of data read from Amazon S3 during analytical queries. By storing data column-wise, Parquet enables predicate pushdown and compression, which minimizes I/O and speeds up data processing in Amazon EMR, especially for large datasets.
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.
- ✗
CSV
Why it's wrong here
CSV is row-oriented and not optimized for analytics.
- ✓
Parquet
Why this is correct
Parquet is columnar, compressed, and ideal for analytics.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
ORC
Why it's wrong here
ORC is also columnar but Parquet is more commonly used with EMR.
- ✗
JSON
Why it's wrong here
JSON is verbose and not columnar.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume ORC is the default or preferred format for all big data engines. However, for Amazon EMR, Parquet is generally recommended because of its superior performance with Spark and its ability to handle complex nested data structures efficiently.
Detailed technical explanation
How to think about this question
Parquet uses a hybrid storage layout that combines row groups and column chunks, allowing each column to be compressed independently with codecs like Snappy or Zstd. In Amazon EMR, Parquet leverages the Spark SQL catalyst optimizer to push down filters (e.g., WHERE clauses) directly to the file metadata, skipping irrelevant row groups entirely. A real-world scenario is processing terabytes of clickstream data where Parquet reduces scan times by over 70% compared to CSV.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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: Parquet — Parquet is the correct choice because it is a columnar storage format that significantly reduces the amount of data read from Amazon S3 during analytical queries. By storing data column-wise, Parquet enables predicate pushdown and compression, which minimizes I/O and speeds up data processing in Amazon EMR, especially for large datasets.
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.
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Last reviewed: Jun 24, 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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