SAP-C02 Parquet format Practice Question
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?
⚠ Common exam trap
Candidates often overlook the cost optimization of storage tiers like S3 Intelligent-Tiering for data lakes with variable access patterns.
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.
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.
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.
- ✗
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.
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 |
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This SAP-C02 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 SAP-C02 exam.