The answer is to convert the data to Parquet format and optimize partitioning. This is the most effective way to improve Athena query performance because Parquet is a columnar storage format that enables efficient compression and allows Athena to scan only the columns needed for a query, dramatically reducing data scanned and I/O. Combined with optimized partitioning, this enables partition pruning, where Athena skips entire directories of irrelevant data based on filter conditions, further minimizing the data processed. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your understanding of how storage format and data organization directly impact query cost and speed in a serverless environment; a common trap is assuming that simply increasing file size or switching to JSON will help, but without columnar storage and proper partitioning, full scans of CSV files remain slow. Remember the memory tip: “Parquet partitions prune perfectly” to recall that format and partitioning work together to boost performance.
DEA-C01 Data Operations and Support Practice Question
This DEA-C01 practice question tests your understanding of data operations and support. 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 uses Amazon Athena to query data in S3. Recently, queries have become slow. The data is stored as CSV files in a partitioned table. What is the most effective way to improve query performance?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Convert the data to Parquet format and optimize partitioning.
Option C is correct because converting to Parquet and partitioning improves compression, columnar scanning, and partition pruning. Option A is wrong because increasing file size alone may not help; large CSV files still require full scans. Option B is wrong because converting to JSON would likely worsen performance. Option D is wrong because more nodes only help with distributed processing, but Athena manages resources automatically.
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 number of nodes in the Athena query engine.
Why it's wrong here
Athena is serverless; you cannot add nodes.
✓
Convert the data to Parquet format and optimize partitioning.
Why this is correct
Parquet is columnar and compressed, improving scan efficiency.
Related concept
Read the scenario before looking for a memorised answer.
Increase the size of the CSV files to reduce the number of files.
Why it's wrong here
Larger files can help, but CSV is still inefficient compared to columnar formats.
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 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
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: Convert the data to Parquet format and optimize partitioning. — Option C is correct because converting to Parquet and partitioning improves compression, columnar scanning, and partition pruning. Option A is wrong because increasing file size alone may not help; large CSV files still require full scans. Option B is wrong because converting to JSON would likely worsen performance. Option D is wrong because more nodes only help with distributed processing, but Athena manages resources automatically.
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 data engineer is investigating why Amazon Athena queries on the 'my-data-lake' bucket are slow. The table is partitioned by year/month/day. The exhibit shows the objects in one partition. What is the MOST likely cause of poor query performance?
easy
✓ A.The files are too small, causing excessive read overhead
B.The files are not compressed
C.The partition columns are not appropriately chosen
D.The data format is CSV instead of Parquet
Why A: Option C is correct because the exhibit shows tiny files (50 bytes), which cause high metadata overhead and slow query performance. Option A is about compression, which cannot be determined. Option B is about partitioning, which is fine. Option D is about format, but CSV is standard.
Variation 2. A data engineer is running an Amazon Athena query that scans a large amount of data in Amazon S3, resulting in high costs. The data is stored in Parquet format in a partitioned table. Which strategy would be MOST effective in reducing the amount of data scanned?
medium
✓ A.Ensure the query includes a WHERE clause that filters on partition columns.
B.Convert the Parquet files to CSV format and apply GZIP compression.
C.Use S3 Intelligent-Tiering storage class to reduce storage costs.
D.Increase the number of partitions by adding more partition columns.
Why A: Option D is correct because using a WHERE clause on partition columns allows Athena to use partition pruning, scanning only the relevant partitions. Option A is incorrect because converting from Parquet to CSV would increase data scanned. Option B is incorrect because increasing the number of partitions without querying on them does not reduce scan. Option C is incorrect because compressing with GZIP reduces storage size but Athena still decompresses and scans the full data if no partition pruning is used.
Variation 3. A data engineer is troubleshooting a slow-running Amazon Athena query on a large dataset stored in S3. The query scans many small files. Which TWO actions can improve query performance?
medium
A.Increase the number of files to increase parallelism
B.Disable S3 server-side encryption
✓ C.Concatenate small files into larger files
✓ D.Partition the data by a frequently filtered column
E.Convert files from CSV to JSON
Why C: Option A is correct because compacting small files into larger ones reduces overhead. Option C is correct because partitioning limits data scanned. Option B is wrong because converting to Parquet helps but is not one of the two selected here (though it is also good). Option D is wrong because more files increase overhead. Option E is wrong because disabling encryption does not affect performance.
Variation 4. A data engineer is troubleshooting a slow-running Amazon Athena query. The query scans a large amount of data. Which TWO actions can improve query performance? (Choose TWO.)
medium
✓ A.Convert the data to Parquet or ORC format.
B.Enable encryption at rest.
C.Increase the Athena query timeout.
✓ D.Partition the table on frequently filtered columns.
E.Use SELECT * to retrieve all columns.
Why A: Partitioning the table and converting to columnar formats like Parquet reduce the amount of data scanned, improving performance. Option C is wrong because using SELECT * scans all columns. Option D is wrong because increasing timeout does not improve performance. Option E is wrong because it is not a standard optimization.
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Question Discussion
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