The answer is to implement partitioning, compression, and columnar storage formats like Parquet or ORC. These three actions directly reduce the amount of data scanned per query, which is the primary driver of both slow performance and high cost in Amazon Athena. Partitioning limits scans to relevant subdirectories, compression shrinks file sizes, and columnar formats allow Athena to read only the necessary columns instead of entire rows. On the AWS Certified Data Engineer Associate DEA-C01 exam, this question tests your understanding that Athena’s pricing and speed are tied to data volume scanned, not compute time—a common trap is choosing to increase workers or timeouts, which only adds cost without reducing scan size. A useful memory tip is “PCC” for Partition, Compress, Columnar—think of it as the three pillars of Athena optimization.
DEA-C01 Data Operations and Support Practice Question
This DEA-C01 practice question tests your understanding of data operations and support. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 runs a data lake on Amazon S3 with AWS Glue and Amazon Athena. The data engineer notices that queries are slow and scanning large amounts of data. Which THREE actions should the engineer take to optimize query performance and reduce costs?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Compress data files using gzip or snappy.
Options A, B, and D are correct. Partitioning reduces data scanned, compressing files reduces scan size, and converting to columnar formats (Parquet) improves performance and reduces cost. Option C is wrong because more workers would increase cost. Option E is wrong because increasing timeout does not improve performance or reduce 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.
✗
Increase the query timeout in Athena.
Why it's wrong here
Does not improve performance; may mask issues.
✗
Increase the number of DPUs in the Glue job.
Why it's wrong here
Increases cost, does not affect Athena queries.
✓
Compress data files using gzip or snappy.
Why this is correct
Reduces storage and data scanned.
Related concept
Read the scenario before looking for a memorised answer.
✓
Partition the data by frequently filtered columns (e.g., date, region).
Why this is correct
Limits data scanned per query.
Related concept
Read the scenario before looking for a memorised answer.
✓
Use columnar data formats like Parquet or ORC.
Why this is correct
Reduces I/O and improves compression.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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.
What to study next
Got this wrong? Here's your next step.
Identify which DEA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Data Operations and Support — This question tests Data Operations and Support — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Compress data files using gzip or snappy. — Options A, B, and D are correct. Partitioning reduces data scanned, compressing files reduces scan size, and converting to columnar formats (Parquet) improves performance and reduces cost. Option C is wrong because more workers would increase cost. Option E is wrong because increasing timeout does not improve performance or reduce cost.
What should I do if I get this DEA-C01 question wrong?
Identify which DEA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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 →
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 company uses Amazon S3 to store large CSV files and runs Amazon Athena queries on them. The queries are becoming slower as data grows. A data engineer suggests converting the files to Apache Parquet format and partitioning the data. What is the primary benefit of converting to Parquet?
medium
A.Parquet allows schema evolution without rewriting files.
B.Parquet supports nested data structures that CSV cannot.
✓ C.Parquet stores data in a columnar format, reducing the amount of data scanned per query.
D.Parquet is compressed by default, reducing storage costs.
Why C: Parquet is a columnar storage format that stores data by columns rather than rows. When Athena queries only a subset of columns, it can read just those columns from disk, drastically reducing the amount of data scanned per query. This directly addresses the performance slowdown because Athena charges by data scanned, and less scanning means faster queries and lower costs.
Variation 2. A data analyst needs to query a large Amazon S3 bucket containing CSV files using Amazon Athena. The bucket has millions of small files (less than 1 MB each). The analyst reports that queries are very slow and often time out. The data is partitioned by date and the partition columns are defined in the table. What is the most effective way to improve query performance?
easy
A.Convert the files to Apache Parquet format using an AWS Glue ETL job.
✓ B.Run a compaction job to consolidate small files into fewer larger files (e.g., 128 MB each).
C.Add more partitions by including hour and minute as partition keys.
D.Use S3 Select to push down filtering to S3 before Athena processes the data.
Why B: Option D is correct because many small files cause high overhead for listing and reading files in Athena. Compacting small files into fewer larger files (e.g., 128 MB each) reduces the I/O operations and improves performance. Option A is wrong because file format conversion (e.g., to Parquet) helps but does not address the small file problem. Option B is wrong because increasing partitions would increase overhead. Option C is wrong because S3 Select is for retrieving subset of data from a single file, not for optimizing many files.
Last reviewed: Jun 20, 2026
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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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