The answer is Amazon Timestream, the most appropriate choice for efficiently handling time-series data from millions of IoT devices. This is correct because Timestream is purpose-built for time-series workloads, using a serverless architecture that automatically scales to support high ingestion rates from batch writes every minute. It optimizes storage by keeping recent data in memory for fast queries and moving historical data to a magnetic store for cost efficiency, while its built-in aggregation functions like `BIN` and `DATE_BIN` compute hourly, daily, and monthly aggregates without manual partitioning or indexing. On the AWS Certified Database Specialty DBS-C01 exam, this scenario tests your understanding of purpose-built databases versus general-purpose options like Amazon RDS or DynamoDB, which lack native time-series optimizations and would require complex sharding or TTL management. A common trap is choosing DynamoDB for its scalability, but Timestream’s automatic tiered storage and built-in time-based aggregations make it the clear winner. Memory tip: think “TimeStream = Time + Stream” — it streams time-series data into memory for recent queries and streams it to magnetic storage for historical aggregates.
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 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.
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 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.
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 designing a database for an IoT application that collects sensor data every second from millions of devices. The data is time-series and must be stored for 90 days, with occasional queries for recent data. Which AWS database solution is MOST cost-effective and performant?
medium
✓ A.Amazon Timestream
B.Amazon Redshift
C.Amazon DynamoDB with TTL to expire old data
D.Amazon RDS for MySQL with partitioning
Why A: Amazon Timestream is purpose-built for time-series data, offering automatic storage tiering (in-memory for recent data and magnetic for historical data) and serverless scaling, which makes it the most cost-effective and performant choice for ingesting sensor data every second from millions of devices and retaining it for 90 days with occasional queries on recent data.
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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