- A
Convert the data to JSON format for better schema evolution.
Why wrong: JSON increases scan size and reduces performance.
- B
Use Glue DataBrew to clean the data before querying.
Why wrong: DataBrew is for data preparation, not performance optimization.
- C
Partition the data by date so Athena can use partition pruning.
Partition pruning limits the amount of data scanned per query.
- D
Ensure the data is in a columnar format like Parquet or ORC.
Columnar formats improve query performance and reduce data scanned.
- E
Compress the data using a codec like Snappy or Gzip.
Compression reduces storage cost and I/O.
Question 1,704 of 1,786
DEA-C01 Data Store Management Practice Question
This DEA-C01 practice question tests your understanding of data store management. 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 uses AWS Glue to catalog data stored in Amazon S3. The data is in Parquet format and partitioned by date. The company wants to improve query performance in Amazon Athena and reduce costs. Which THREE actions should the company take? (Choose THREE.)
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
✓
Partition the data by date so Athena can use partition pruning.
Option C is correct because partitioning the data by date allows Athena to use partition pruning, which limits the amount of data scanned by only reading the partitions that match the query's WHERE clause. This directly reduces both query cost (since Athena charges per byte scanned) and query latency, especially for date-range queries on large datasets.
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.
- ✗
Convert the data to JSON format for better schema evolution.
Why it's wrong here
JSON increases scan size and reduces performance.
- ✗
Use Glue DataBrew to clean the data before querying.
Why it's wrong here
DataBrew is for data preparation, not performance optimization.
- ✓
Partition the data by date so Athena can use partition pruning.
Why this is correct
Partition pruning limits the amount of data scanned per query.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Ensure the data is in a columnar format like Parquet or ORC.
Why this is correct
Columnar formats improve query performance and reduce data scanned.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Compress the data using a codec like Snappy or Gzip.
Why this is correct
Compression reduces storage cost and I/O.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse data preparation tools (like Glue DataBrew) with query optimization techniques, or mistakenly think that converting to a non-columnar format like JSON improves schema evolution, when in fact columnar formats with compression and partitioning are the standard best practices for Athena performance and cost efficiency.
Detailed technical explanation
How to think about this question
Parquet and ORC are columnar storage formats that store data by column rather than by row, enabling Athena to read only the columns needed for a query, which drastically reduces I/O and decompression overhead. Snappy and Gzip compression further reduce storage size and data scanned, with Snappy offering faster decompression for query performance and Gzip providing higher compression ratios. Partition pruning works by leveraging Hive-style partitioning (e.g., s3://bucket/table/dt=2025-01-01/), and Athena's query engine reads only the partition directories that match the filter conditions, minimizing S3 LIST and GET requests.
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.
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
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.
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.
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FAQ
Questions learners often ask
What does this DEA-C01 question test?
Data Store Management — This question tests Data Store Management — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Partition the data by date so Athena can use partition pruning. — Option C is correct because partitioning the data by date allows Athena to use partition pruning, which limits the amount of data scanned by only reading the partitions that match the query's WHERE clause. This directly reduces both query cost (since Athena charges per byte scanned) and query latency, especially for date-range queries on large datasets.
What should I do if I get this DEA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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