- A
Use Amazon DynamoDB with a table keyed by device ID (partition) and timestamp (sort).
DynamoDB supports massive write throughput and efficient querying by device and time range.
- B
Use Amazon RDS for MySQL with Multi-AZ and auto-scaling storage.
Why wrong: RDS cannot handle millions of writes per second and is not designed for IoT ingestion rates.
- C
Use Amazon Redshift with a schema optimized for time-series data.
Why wrong: Redshift is for analytics and batch loading, not real-time high-volume writes.
- D
Use Amazon ElastiCache for Redis with persistence enabled.
Why wrong: ElastiCache is in-memory and costly for large historical datasets.
Question 363 of 1,730
DBS-C01 Workload-Specific Database Design Practice Question
This DBS-C01 practice question tests your understanding of workload-specific database design. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. 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 company is designing a database for a global IoT application that ingests millions of events per second. Each event includes a device ID, timestamp, and sensor readings. The requirement is to store data for historical analysis and to support queries that aggregate data by device ID over time ranges. The team needs a cost-effective solution that can scale write throughput. Which database design is most appropriate?
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
✓
Use Amazon DynamoDB with a table keyed by device ID (partition) and timestamp (sort).
Amazon DynamoDB with a composite primary key of device ID (partition key) and timestamp (sort key) is ideal for this IoT workload because it provides scalable write throughput to handle millions of events per second, while the sort key enables efficient time-range queries and aggregation by device ID. DynamoDB's fully managed, serverless architecture ensures cost-effectiveness by automatically scaling capacity and charging only for consumed resources, making it suitable for high-velocity time-series data.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use Amazon DynamoDB with a table keyed by device ID (partition) and timestamp (sort).
Why this is correct
DynamoDB supports massive write throughput and efficient querying by device and time range.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use Amazon RDS for MySQL with Multi-AZ and auto-scaling storage.
Why it's wrong here
RDS cannot handle millions of writes per second and is not designed for IoT ingestion rates.
- ✗
Use Amazon Redshift with a schema optimized for time-series data.
Why it's wrong here
Redshift is for analytics and batch loading, not real-time high-volume writes.
- ✗
Use Amazon ElastiCache for Redis with persistence enabled.
Why it's wrong here
ElastiCache is in-memory and costly for large historical datasets.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose Amazon RDS or Redshift because they are familiar with SQL and time-series databases, but they overlook the critical requirement for extreme write scalability and cost-effectiveness that DynamoDB's serverless model provides for IoT workloads.
Detailed technical explanation
How to think about this question
DynamoDB's adaptive capacity automatically redistributes partitions to handle hot keys, ensuring consistent write performance even when a single device ID generates a high volume of events. The sort key on timestamp allows use of the Query API with KeyConditionExpression to efficiently retrieve events within a specific time range, and global secondary indexes can be added for alternative access patterns without affecting write throughput. In practice, this design supports time-series patterns like downsampling or TTL-based data expiration to manage storage costs.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
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
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this DBS-C01 question test?
Workload-Specific Database Design — This question tests Workload-Specific Database Design — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use Amazon DynamoDB with a table keyed by device ID (partition) and timestamp (sort). — Amazon DynamoDB with a composite primary key of device ID (partition key) and timestamp (sort key) is ideal for this IoT workload because it provides scalable write throughput to handle millions of events per second, while the sort key enables efficient time-range queries and aggregation by device ID. DynamoDB's fully managed, serverless architecture ensures cost-effectiveness by automatically scaling capacity and charging only for consumed resources, making it suitable for high-velocity time-series data.
What should I do if I get this DBS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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