- A
Use AWS Glue Data Catalog to store the table metadata.
Why wrong: Required but not an optimization step; it's foundational.
- B
Store the data in JSON format for flexibility.
Why wrong: JSON is not optimized for query performance; columnar formats are better.
- C
Convert the data to Apache Parquet or ORC format.
Columnar formats reduce data scanned and improve compression.
- D
Compress the data using gzip or snappy.
Compression reduces storage and scanning costs.
- E
Partition the data by date in S3 (e.g., year/month/day).
Partitioning allows Athena to scan only relevant partitions, reducing cost.
Question 862 of 1,786
Three Steps to Optimize Athena Query Performance and Cost
This DEA-C01 practice question tests your understanding of data ingestion and transformation. 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. A key principle to apply: columnar Storage Formats. 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.
An e-commerce company is building a near-real-time dashboard to monitor customer clickstream data. The data is ingested via Amazon Kinesis Data Streams, transformed using AWS Lambda, and stored in Amazon S3. The team needs to query the data using Amazon Athena. Which THREE steps should be taken to optimize cost and performance? (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
✓
Convert the data to Apache Parquet or ORC format.
To optimize cost and performance when querying data with Athena, use columnar formats like Parquet or ORC (C) to reduce data scanned and improve compression. Compress data with gzip or Snappy (D) to reduce storage costs and data transferred during queries. Partition data by date (E) to limit the amount of data scanned per query. Option A (Glue Data Catalog) is a prerequisite, not an optimization step. Option B (JSON) is less efficient than columnar formats for analytical queries.
Key principle: Columnar Storage Formats
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use AWS Glue Data Catalog to store the table metadata.
Why it's wrong here
Required but not an optimization step; it's foundational.
- ✗
Store the data in JSON format for flexibility.
Why it's wrong here
JSON is not optimized for query performance; columnar formats are better.
- ✓
Convert the data to Apache Parquet or ORC format.
Why this is correct
Columnar formats reduce data scanned and improve compression.
Related concept
Columnar Storage Formats
- ✓
Compress the data using gzip or snappy.
Why this is correct
Compression reduces storage and scanning costs.
Related concept
Columnar Storage Formats
- ✓
Partition the data by date in S3 (e.g., year/month/day).
Why this is correct
Partitioning allows Athena to scan only relevant partitions, reducing cost.
Related concept
Columnar Storage Formats
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common trap is to consider AWS Glue Data Catalog as an optimization step, but it is merely a requirement; the actual optimizations are compression, partitioning, and columnar formats.
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 Formats
- Data Compression
- Partitioning
- Amazon Athena
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 Formats
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.
Review columnar Storage Formats, then practise related DEA-C01 questions on the same topic to reinforce the concept.
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Data Ingestion and Transformation — study guide chapter
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Data Ingestion and Transformation practice questions
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FAQ
Questions learners often ask
What does this DEA-C01 question test?
Data Ingestion and Transformation — This question tests Data Ingestion and Transformation — Columnar Storage Formats.
What is the correct answer to this question?
The correct answer is: Convert the data to Apache Parquet or ORC format. — To optimize cost and performance when querying data with Athena, use columnar formats like Parquet or ORC (C) to reduce data scanned and improve compression. Compress data with gzip or Snappy (D) to reduce storage costs and data transferred during queries. Partition data by date (E) to limit the amount of data scanned per query. Option A (Glue Data Catalog) is a prerequisite, not an optimization step. Option B (JSON) is less efficient than columnar formats for analytical queries.
What should I do if I get this DEA-C01 question wrong?
Review columnar Storage Formats, then practise related DEA-C01 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Columnar Storage Formats
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Last reviewed: Jun 20, 2026
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