- A
Increase the number of nodes in the Athena query engine.
Why wrong: Increasing the number of nodes is not possible in Athena; it is serverless and scales automatically. This option is incorrect.
- B
Convert the data to Parquet format and optimize partitioning.
Converting to Parquet (columnar) and optimizing partitioning reduces data scanned through column pruning and partition pruning, significantly improving performance. This is the correct answer.
- C
Convert the data to JSON format.
Why wrong: Converting to JSON is a row-based format, often more verbose than CSV, leading to even larger data scans and worse performance. This option is incorrect.
- D
Increase the size of the CSV files to reduce the number of files.
Why wrong: Increasing CSV file size alone does not help because CSV is row-based and still requires full scans. Parquet and proper partitioning are needed. This option is incorrect.
DEA-C01 Columnar Storage Practice Question
This DEA-C01 practice question tests your understanding of data operations and support. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. A key principle to apply: columnar Storage. 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 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
Convert the data to Parquet format and optimize partitioning.
Option B is the correct answer. Parquet is a columnar storage format that allows Athena to read only the columns needed for a query, reducing I/O and improving performance. Combined with effective partitioning, it enables partition pruning, which further limits the data scanned. CSV files are row-based and require full scans, even with partitioning. Option A is incorrect because Athena is serverless and users cannot increase nodes; resources are managed automatically. Option C is incorrect: JSON is also row-based and verbose, making it even slower than CSV. Option D is incorrect because larger CSV files still lead to full scans; Parquet's columnar nature is more impactful than file size.
Key principle: Columnar Storage
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
Increasing the number of nodes is not possible in Athena; it is serverless and scales automatically. This option is incorrect.
- ✓
Convert the data to Parquet format and optimize partitioning.
Why this is correct
Converting to Parquet (columnar) and optimizing partitioning reduces data scanned through column pruning and partition pruning, significantly improving performance. This is the correct answer.
Related concept
Columnar Storage
- ✗
Convert the data to JSON format.
Why it's wrong here
Converting to JSON is a row-based format, often more verbose than CSV, leading to even larger data scans and worse performance. This option is incorrect.
- ✗
Increase the size of the CSV files to reduce the number of files.
Why it's wrong here
Increasing CSV file size alone does not help because CSV is row-based and still requires full scans. Parquet and proper partitioning are needed. This option is incorrect.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common trap is to think that simply increasing file size or using a more popular format like JSON will help. However, the key is switching to a columnar format (Parquet or ORC) that minimizes data scanned.
Detailed technical explanation
How to think about this question
Treat this as a scenario question. Identify the problem, the constraint, and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- Columnar Storage
- Partition Pruning
- Row-based Formats (CSV, JSON)
- Athena Performance Tuning
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
Columnar Storage
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.
Review columnar Storage, then practise related DEA-C01 questions on the same topic to reinforce the concept.
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FAQ
Questions learners often ask
What does this DEA-C01 question test?
Data Operations and Support — This question tests Data Operations and Support — Columnar Storage.
What is the correct answer to this question?
The correct answer is: Convert the data to Parquet format and optimize partitioning. — Option B is the correct answer. Parquet is a columnar storage format that allows Athena to read only the columns needed for a query, reducing I/O and improving performance. Combined with effective partitioning, it enables partition pruning, which further limits the data scanned. CSV files are row-based and require full scans, even with partitioning. Option A is incorrect because Athena is serverless and users cannot increase nodes; resources are managed automatically. Option C is incorrect: JSON is also row-based and verbose, making it even slower than CSV. Option D is incorrect because larger CSV files still lead to full scans; Parquet's columnar nature is more impactful than file size.
What should I do if I get this DEA-C01 question wrong?
Review columnar Storage, then practise related DEA-C01 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Columnar Storage
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Last reviewed: Jun 20, 2026
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