DBS-C01 Workload-Specific Database Design Practice Question
A company is designing a data lake on Amazon S3 with Amazon Redshift Spectrum for analytics. The data includes JSON logs from web servers. Which THREE design practices should the company follow to optimize query performance and cost?
⚠ Common exam trap
The DBS-C01 exam often tests the misconception that more files equals more parallelism, but in Redshift Spectrum, excessive small files increase overhead and reduce performance, while the correct approach is to use fewer, larger files in a columnar format with partitioning.
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 files using gzip or snappy.
Compressing JSON files with gzip or Snappy reduces the data size stored in S3, lowering storage costs and minimizing the amount of data that Redshift Spectrum must scan over the network. Redshift Spectrum can read compressed files directly, and compression often improves query performance by reducing I/O, even though it adds a small CPU overhead for decompression.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Compress files using gzip or snappy.
Why this is correct
Compression reduces storage and I/O.
- ✗
Use many small files to maximize parallelism.
Why it's wrong here
Many small files increase overhead; larger files are better.
- ✓
Partition the data by date (e.g., year/month/day) in S3.
Why this is correct
Partitioning limits the data scanned by queries.
- ✓
Convert JSON files to Apache Parquet format.
Why this is correct
Columnar formats are more efficient for analytics.
- ✗
Create indexes on the S3 data using AWS Glue.
Why it's wrong here
Redshift Spectrum does not use indexes on S3.
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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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DBS-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 DBS-C01 exam.