DBS-C01 Workload-Specific Database Design Practice Question
A company wants to store and analyze time-series sensor data from millions of IoT devices. The data is append-only and rarely updated. Queries aggregate data over time ranges. Which AWS database service is most cost-effective and performant for this workload?
⚠ Common exam trap
Test-takers frequently choose DynamoDB due to its familiarity and scalability, overlooking that Timestream is purpose-built for time-series workloads and offers automatic tiered storage and optimized query performance, which DynamoDB lacks without significant custom engineering.
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
✓
Amazon Timestream
Amazon Timestream is purpose-built for time-series data, offering automatic tiered storage (in-memory for recent data and magnetic for historical data) and optimized query performance for time-range aggregations. Its serverless architecture eliminates provisioning overhead, making it the most cost-effective and performant choice for append-only IoT sensor data with infrequent updates.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Amazon DynamoDB with time-series design pattern
Why it's wrong here
DynamoDB can be used but is less efficient and more expensive for large-scale time-series.
- ✓
Amazon Timestream
Why this is correct
Amazon Timestream is a fast, scalable, fully managed time-series database service.
- ✗
Amazon Redshift
Why it's wrong here
Amazon Redshift is designed for petabyte-scale analytic workloads requiring complex joins and mixed read-write patterns, but its columnar storage and node-based architecture incur higher per-GB costs and provisioning overhead than a purpose-built time-series database. The append-only, rarely updated nature of IoT sensor data and the need for efficient range scans over time-series data are better served by Amazon Timestream, which automatically partitions and ages data by time. Redshift is tempting because it handles large-scale aggregation queries, and would be correct for a data warehouse combining IoT data with relational sources for business intelligence.
- ✗
Amazon RDS for MySQL with partitioning by date
Why it's wrong here
RDS is relational and not optimized for append-heavy time-series workloads.
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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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.