Quick Answer
Parquet is the correct choice because its columnar storage format dramatically reduces the amount of data read from Amazon S3 during analytical queries, directly addressing the need to optimize S3 read performance with Parquet in Amazon EMR. By storing data column-wise rather than row-wise, Parquet enables predicate pushdown—where only the columns relevant to a query are scanned—and leverages efficient compression, which minimizes I/O and accelerates processing for large datasets. On the AWS Certified Data Engineer Associate DEA-C01 exam, this question tests your understanding of how file format selection impacts EMR job performance and S3 costs; a common trap is choosing Avro or JSON, which are row-oriented and lack the same read optimization for analytics. Remember that Parquet is ideal for read-heavy, column-based workloads, while Avro suits write-heavy, row-based scenarios. Memory tip: think “Parquet for queries, Avro for archives.”
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?
CSV
Why wrong: CSV is row-oriented and not optimized for analytics.
Parquet
Parquet is columnar, compressed, and ideal for analytics.
ORC
Why wrong: ORC is also columnar but Parquet is more commonly used with EMR.
JSON
Why wrong: JSON is verbose and not columnar.
Answer choices
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
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
The trap here is that candidates often confuse ORC and Parquet as equally optimal for all engines, but Cisco tests that Parquet is the recommended columnar format for Amazon EMR due to its tighter integration with Spark and better performance on S3.
Detailed technical explanation
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.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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.
What to study next
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FAQ
Data Store Management — This question tests Data Store Management — Read the scenario before looking for a memorised answer..
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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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