SAP-C02 Design for New Solutions Practice Question
A company is designing a new data lake on AWS. The data lake will store raw data from various sources in Amazon S3. The data will be processed using AWS Glue ETL jobs and queried using Amazon Athena. To optimize costs and performance, which three practices should the solutions architect implement?
⚠ Common exam trap
It's easy for candidates to choose JSON for its flexibility without realizing its severe cost and performance penalties in analytics workloads, or they mistakenly believe many small files improve parallelism, when in fact they cause S3 request throttling and increased Athena query overhead.
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
✓
Compress data using Snappy or Gzip compression.
Compressing data with Snappy or Gzip reduces storage costs in Amazon S3 and decreases the amount of data scanned by Athena, which charges per TB scanned. Snappy offers faster decompression for ETL workloads, while Gzip provides higher compression ratios. Both are natively supported by AWS Glue and Athena, making them optimal for cost and performance.
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 JSON format for flexibility.
Why it's wrong here
JSON is row-based and increases scan size.
- ✓
Compress data using Snappy or Gzip compression.
Why this is correct
Compression reduces storage and scan costs.
- ✓
Use columnar storage formats such as Parquet or ORC.
Why this is correct
Columnar formats reduce scan and improve compression.
- ✗
Store data in many small files to improve parallel processing.
Why it's wrong here
Many small files increase overhead; use larger files.
- ✓
Partition the data by date and other high-cardinality columns.
Why this is correct
Partition pruning reduces data scanned.
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 |
Go deeper
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