- A
Store data in many small files to increase parallelism.
Why wrong: Many small files increase overhead and Athena charges per query.
- B
Partition the data by a commonly used filter column.
Partition pruning limits the data scanned.
- C
Use S3 Select instead of Athena for queries.
Why wrong: S3 Select is not a replacement for Athena; it's a different service.
- D
Compress data with a splittable compression format like Snappy.
Compression reduces storage and I/O.
- E
Convert data to Apache Parquet or ORC format.
Columnar formats reduce data scanned.
Question 1,688 of 1,786
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 data engineer is designing a data lake on Amazon S3. The data is ingested from multiple sources and must be queryable using Amazon Athena. The engineer needs to optimize query performance and reduce costs. Which THREE actions would achieve this?
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 a commonly used filter column.
Partitioning data by a commonly used filter column (e.g., date, region) allows Athena to prune partitions during query execution, scanning only the relevant S3 prefixes. This reduces the amount of data read per query, directly lowering both query latency and cost, as Athena charges based on the volume of data scanned.
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.
- ✗
Store data in many small files to increase parallelism.
Why it's wrong here
Many small files increase overhead and Athena charges per query.
- ✓
Partition the data by a commonly used filter column.
Why this is correct
Partition pruning limits the data scanned.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use S3 Select instead of Athena for queries.
Why it's wrong here
S3 Select is not a replacement for Athena; it's a different service.
- ✓
Compress data with a splittable compression format like Snappy.
Why this is correct
Compression reduces storage and I/O.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Convert data to Apache Parquet or ORC format.
Why this is correct
Columnar formats reduce data scanned.
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 confuse 'more files = more parallelism' with Athena's actual recommendation of fewer, larger files to minimize the overhead of S3 list and get operations, and they may also mistake S3 Select as a viable alternative to Athena for full SQL querying.
Detailed technical explanation
How to think about this question
Athena leverages the Hive metastore's partition pruning to skip entire directories when a query's WHERE clause matches a partition key. For example, partitioning by `year/month/day` allows Athena to read only the day's data when querying a specific date. Additionally, columnar formats like Parquet store data in row groups with embedded min/max statistics, enabling predicate pushdown at the file level for even finer-grained data skipping.
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 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: Partition the data by a commonly used filter column. — Partitioning data by a commonly used filter column (e.g., date, region) allows Athena to prune partitions during query execution, scanning only the relevant S3 prefixes. This reduces the amount of data read per query, directly lowering both query latency and cost, as Athena charges based on the volume of data scanned.
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
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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