DEA-C01 Data Store Management Practice Question
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?
⚠ Common exam trap
Many candidates 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.
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.
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 plain text with no compression, columnar layout or statistics, so EMR reads every field of every row. Parquet stores columns separately with metadata enabling predicate pushdown. CSV suits small files or simple interchange where human readability outweighs query performance.
- ✓
Parquet
Why this is correct
Parquet is columnar and compressed, so EMR reads only the columns referenced by the query rather than scanning entire rows. This column pruning plus predicate pushdown and efficient encoding cuts the volume of data transferred from S3, directly reducing read time.
- ✗
ORC
Why it's wrong here
ORC is columnar and compresses well, but it is optimised for Hive and Presto rather than the Spark-centric EMR workloads described. It is tempting because it is a columnar format, and it would be correct for Hive-heavy processing, yet Parquet's Spark integration and predicate pushdown cut S3 read time further.
- ✗
JSON
Why it's wrong here
JSON is row-oriented text without columnar storage or embedded statistics, so EMR must parse every record and cannot skip irrelevant columns. Parquet or ORC store data columnar with predicate pushdown. JSON suits semi-structured interchange or nested API payloads where schema flexibility matters more than scan speed.
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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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.