- A
Store data as gzip-compressed JSON in S3, partition by device_id, and use Athena with compression.
Why wrong: Gzip is not columnar; many partitions degrade performance.
- B
Convert data to Parquet, partition by date, and use S3 Intelligent-Tiering.
Parquet reduces scan, partitioning limits data, Intelligent-Tiering optimizes cost.
- C
Convert data to Parquet format, partition by year/month/day, and use S3 Standard storage.
Why wrong: Good but S3 Standard may be costly for infrequent access.
- D
Store data as Parquet in S3 Glacier Deep Archive, unpartitioned, and query with Athena.
Why wrong: Glacier retrieval makes queries slow and expensive.
Question 1,348 of 1,746
SAP-C02 Parquet format Practice Question
This SAP-C02 practice question tests your understanding of design for new solutions. 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: parquet format. 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 startup is designing a data lake on AWS using Amazon S3. They expect to ingest hundreds of terabytes of data from IoT devices daily. Data is in JSON format and will be queried using Amazon Athena. Which combination of actions will optimize query performance and minimize costs?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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 data to Parquet, partition by date, and use S3 Intelligent-Tiering.
Option B is correct because converting data to Parquet format provides columnar storage and efficient compression, reducing the amount of data scanned by Athena. Partitioning by date (e.g., year/month/day) aligns with typical query patterns for time-series IoT data, allowing Athena to skip irrelevant partitions and minimize costs. S3 Intelligent-Tiering automatically moves data between access tiers to optimize storage costs without manual intervention. Option A is incorrect because gzip-compressed JSON is not as efficient as Parquet in terms of compression and query performance, and partitioning by device_id would create too many small files (poor partitioning). Option C is incorrect because while Parquet and date partitioning are good, S3 Standard is more expensive for data that may not be accessed frequently after initial queries; Intelligent-Tiering is more cost-effective. Option D is incorrect because S3 Glacier Deep Archive has high retrieval costs and long retrieval times, making it unsuitable for frequent Athena queries, and lack of partitioning leads to full table scans.
Key principle: Parquet format
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 as gzip-compressed JSON in S3, partition by device_id, and use Athena with compression.
Why it's wrong here
Gzip is not columnar; many partitions degrade performance.
- ✓
Convert data to Parquet, partition by date, and use S3 Intelligent-Tiering.
Why this is correct
Parquet reduces scan, partitioning limits data, Intelligent-Tiering optimizes cost.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Parquet format
- ✗
Convert data to Parquet format, partition by year/month/day, and use S3 Standard storage.
Why it's wrong here
Good but S3 Standard may be costly for infrequent access.
- ✗
Store data as Parquet in S3 Glacier Deep Archive, unpartitioned, and query with Athena.
Why it's wrong here
Glacier retrieval makes queries slow and expensive.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates often overlook the cost optimization of storage tiers like S3 Intelligent-Tiering for data lakes with variable access patterns.
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
- Parquet format
- Partitioning in Athena
- S3 Intelligent-Tiering
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
Parquet format
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 parquet format, then practise related SAP-C02 questions on the same topic to reinforce the concept.
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Design for New Solutions — study guide chapter
Learn the concepts, then practise the questions
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Design for New Solutions practice questions
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FAQ
Questions learners often ask
What does this SAP-C02 question test?
Design for New Solutions — This question tests Design for New Solutions — Parquet format.
What is the correct answer to this question?
The correct answer is: Convert data to Parquet, partition by date, and use S3 Intelligent-Tiering. — Option B is correct because converting data to Parquet format provides columnar storage and efficient compression, reducing the amount of data scanned by Athena. Partitioning by date (e.g., year/month/day) aligns with typical query patterns for time-series IoT data, allowing Athena to skip irrelevant partitions and minimize costs. S3 Intelligent-Tiering automatically moves data between access tiers to optimize storage costs without manual intervention. Option A is incorrect because gzip-compressed JSON is not as efficient as Parquet in terms of compression and query performance, and partitioning by device_id would create too many small files (poor partitioning). Option C is incorrect because while Parquet and date partitioning are good, S3 Standard is more expensive for data that may not be accessed frequently after initial queries; Intelligent-Tiering is more cost-effective. Option D is incorrect because S3 Glacier Deep Archive has high retrieval costs and long retrieval times, making it unsuitable for frequent Athena queries, and lack of partitioning leads to full table scans.
What should I do if I get this SAP-C02 question wrong?
Review parquet format, then practise related SAP-C02 questions on the same topic to reinforce the concept.
Are there clue words in this question I should notice?
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
What is the key concept behind this question?
Parquet format
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Last reviewed: Jun 20, 2026
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