- A
Amazon DynamoDB with TTL
Why wrong: DynamoDB can store time-series but requires careful design and does not have built-in aggregation functions.
- B
Amazon Redshift
Why wrong: Redshift is for analytical queries on large datasets, not real-time ingestion.
- C
Amazon RDS for PostgreSQL
Why wrong: RDS is not optimized for high-ingestion time-series workloads.
- D
Amazon Timestream
Timestream is purpose-built for time-series data, with automatic storage tiering and aggregate functions.
Question 599 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 runs a time-series application that collects sensor data from millions of IoT devices. The data is written in batches every minute and queried to generate hourly, daily, and monthly aggregates. The database must support high ingestion rates and efficient storage. Which database service 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
✓
Amazon Timestream
Amazon Timestream is purpose-built for time-series data, offering a serverless architecture that automatically scales to handle high ingestion rates from millions of IoT devices. It optimizes storage by separating recent data (in memory) from historical data (in a magnetic store), and its built-in aggregation functions (e.g., `BIN`, `DATE_BIN`) efficiently compute hourly, daily, and monthly aggregates without manual partitioning or indexing.
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.
- ✗
Amazon DynamoDB with TTL
Why it's wrong here
DynamoDB can store time-series but requires careful design and does not have built-in aggregation functions.
- ✗
Amazon Redshift
Why it's wrong here
Redshift is for analytical queries on large datasets, not real-time ingestion.
- ✗
Amazon RDS for PostgreSQL
Why it's wrong here
RDS is not optimized for high-ingestion time-series workloads.
- ✓
Amazon Timestream
Why this is correct
Timestream is purpose-built for time-series data, with automatic storage tiering and aggregate functions.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose Amazon DynamoDB for high ingestion rates, overlooking that time-series workloads require efficient time-based aggregation and storage optimization, which DynamoDB lacks, while Timestream is the only AWS service purpose-built for this exact use case.
Detailed technical explanation
How to think about this question
Amazon Timestream uses a dual-storage architecture: a memory store for recent data (configurable retention, e.g., 24 hours) enabling fast writes and queries, and a magnetic store for historical data with automatic compression and tiering. Its query engine supports time-series-specific functions like `INTERPOLATE_LINEAR` for gap filling and `SMOOTH` for moving averages, which are not available in general-purpose databases. In a real-world scenario, a fleet of 10 million IoT sensors writing 100-byte records per minute would generate ~1 TB of data daily, which Timestream can ingest and query without manual sharding or partitioning.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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: Amazon Timestream — Amazon Timestream is purpose-built for time-series data, offering a serverless architecture that automatically scales to handle high ingestion rates from millions of IoT devices. It optimizes storage by separating recent data (in memory) from historical data (in a magnetic store), and its built-in aggregation functions (e.g., `BIN`, `DATE_BIN`) efficiently compute hourly, daily, and monthly aggregates without manual partitioning or indexing.
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: Jun 24, 2026
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