Amazon Timestream is the correct choice because it is purpose-built for IoT time-series data, offering automatic tiered storage that keeps recent data in memory for fast queries and moves historical data to magnetic storage at lower cost, while its built-in aggregation functions handle millions of writes per second without manual sharding or TTL management. On the AWS Certified Database Specialty DBS-C01 exam, this scenario tests your understanding of purpose-built databases versus general-purpose options like DynamoDB or RDS, which would require complex partitioning and retention policies for time-series workloads. A common trap is choosing DynamoDB with TTL, but Timestream’s serverless model and optimized time-based aggregations make it far more cost-effective for real-time dashboards querying the last hour of data. Memory tip: think “TimeStream = Time + Stream” — it streams recent data in memory and streams old data to magnetic, saving you from manual stream management.
DBS-C01 Workload-Specific Database Design Practice Question
This DBS-C01 practice question tests your understanding of workload-specific database design. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 building a real-time analytics dashboard from IoT sensor data. Data arrives as time-series with millions of writes per second. The dashboard queries the last hour of data with aggregations. Which database design is most cost-effective?
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) and built-in aggregation functions optimized for time-based queries. This design handles millions of writes per second cost-effectively, as it eliminates the need for manual sharding or TTL management, and its serverless model charges only for data written and queried, making it ideal for real-time analytics on the last hour of IoT sensor 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.
✗
Amazon DynamoDB with TTL and DAX
Why it's wrong here
DynamoDB write costs are high for millions writes/sec, and DAX helps reads only.
✗
Amazon Redshift with streaming ingestion
Why it's wrong here
Redshift is for batch analytics, higher latency and cost.
✓
Amazon Timestream
Why this is correct
Optimized for time-series with low cost for high write throughput and efficient recent data queries.
Related concept
Read the scenario before looking for a memorised answer.
✗
Amazon RDS for PostgreSQL with TimescaleDB extension
Why it's wrong here
RDS has write throughput limits and scaling challenges at millions writes/sec.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose DynamoDB with TTL and DAX because they associate it with high write throughput and caching, but they overlook that time-series aggregation queries require native time-based functions and cost-efficient storage tiering, which Timestream uniquely provides.
Detailed technical explanation
How to think about this question
Timestream uses a two-tier storage model: a memory store for recent data (configurable retention, default 24 hours) and a magnetic store for historical data, automatically moving data between tiers without user intervention. Its query engine leverages a time-based partitioning scheme and built-in functions like `BIN()` and `INTERPOLATE_LINEAR()` to compute aggregations (e.g., AVG, SUM) over time windows efficiently, avoiding full table scans. In a real-world scenario, a company ingesting 10 million sensor writes per second would pay only for the data volume and queries, with no idle compute costs, and can query the last hour with sub-second latency using Timestream’s optimized time-series query execution.
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.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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 automatic tiered storage (in-memory for recent data and magnetic for historical) and built-in aggregation functions optimized for time-based queries. This design handles millions of writes per second cost-effectively, as it eliminates the need for manual sharding or TTL management, and its serverless model charges only for data written and queried, making it ideal for real-time analytics on the last hour of IoT sensor 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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company is building a real-time analytics dashboard for IoT sensor data. The data arrives as JSON and needs to be stored in a way that supports fast ingestion and complex queries. Which database service is best suited?
easy
A.Amazon RDS for PostgreSQL
B.Amazon DynamoDB with TTL
✓ C.Amazon Timestream
D.Amazon Redshift
Why C: Amazon Timestream is a time-series database optimized for IoT data, with fast ingestion and built-in analytics functions. DynamoDB is not optimized for time-series queries. RDS is not designed for high-velocity time-series data. Redshift is for batch analytics.
Last reviewed: Jun 24, 2026
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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.
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