Quick Answer
Amazon Timestream is the correct choice because it is a purpose-built, serverless time-series database that automatically manages data across in-memory and magnetic storage tiers, making it the most cost-effective and scalable AWS service for IoT sensor workloads that require continuous writes and time-range dashboard queries. On the AWS Certified Database Specialty DBS-C01 exam, this scenario tests your understanding of when to choose a specialized database over a general-purpose one like DynamoDB or RDS, which lack native time-series optimizations and can become expensive under high-ingestion rates. A common trap is selecting DynamoDB with TTL for time-series data, but Timestream’s built-in aggregation functions, automatic retention policies, and tiered storage deliver far better cost efficiency for this pattern. Remember the mnemonic “T.I.M.E.” — Tiered storage, Ingest continuous, Measure intervals, Efficient queries — to recall why Timestream wins for IoT time-series workloads.
This DBS-C01 practice question tests your understanding of workload-specific database design. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 needs to store and query time-series data from IoT sensors. The data is written continuously and queried by time range for dashboards. Which AWS database service is most cost-effective and scalable for this workload?
Amazon ElastiCache for Redis with time-series data structures.
Why wrong: In-memory, expensive for large volumes.
Amazon DynamoDB with time-based partition keys.
Why wrong: Can be used but not optimal; requires careful key design.
Amazon Timestream.
Managed time-series database with built-in analytics.
Amazon RDS for PostgreSQL with time-based indexing.
Why wrong: Not purpose-built; may have performance issues at scale.
Answer choices
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 tiering between in-memory and magnetic stores for cost efficiency, and built-in functions for time-based aggregations and windowed queries. It is serverless and scales automatically to handle continuous writes from IoT sensors and low-latency dashboard queries by time range, making it the most cost-effective and scalable choice.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Amazon ElastiCache for Redis with time-series data structures.
Why it's wrong here
In-memory, expensive for large volumes.
Amazon DynamoDB with time-based partition keys.
Why it's wrong here
Can be used but not optimal; requires careful key design.
Amazon Timestream.
Why this is correct
Managed time-series database with built-in analytics.
Related concept
Read the scenario before looking for a memorised answer.
Amazon RDS for PostgreSQL with time-based indexing.
Why it's wrong here
Not purpose-built; may have performance issues at scale.
Common exam traps
The trap here is that candidates often choose DynamoDB for its scalability, overlooking that time-series workloads with sequential timestamps cause hot partitions and lack native time-series query capabilities, while Timestream is the only service specifically designed for this use case with automatic tiering and cost optimization.
Detailed technical explanation
Amazon Timestream uses a purpose-built storage engine that separates recent data in an in-memory store for fast ingestion and queries, and automatically moves older data to a cost-optimized magnetic store. It supports SQL with time-series extensions like `BIN()` and `INTERPOLATE_LINEAR()`, enabling efficient windowed aggregations without complex ETL. In a real-world scenario with millions of IoT sensors, Timestream can ingest hundreds of thousands of records per second and return dashboard queries in sub-second latency by leveraging its adaptive indexing and automatic retention policies.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Workload-Specific Database Design — This question tests Workload-Specific Database Design — Read the scenario before looking for a memorised answer..
The correct answer is: Amazon Timestream. — Amazon Timestream is purpose-built for time-series data, offering automatic tiering between in-memory and magnetic stores for cost efficiency, and built-in functions for time-based aggregations and windowed queries. It is serverless and scales automatically to handle continuous writes from IoT sensors and low-latency dashboard queries by time range, making it the most cost-effective and scalable choice.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Same concept, more angles
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 needs to store and query time-series data from IoT devices. The data arrives in high volume and requires efficient range queries over time. Which database is most appropriate?
easyWhy B: Amazon Timestream is a purpose-built time-series database that automatically scales to handle high-volume IoT data and is optimized for efficient range queries over time. It separates storage into a memory store for recent data and a magnetic store for historical data, enabling fast queries across time ranges with built-in time-series functions.
Variation 2. A company needs to store time-series data from IoT sensors. Each sensor sends a reading every minute. The data is rarely accessed after 30 days. The query pattern is to retrieve all readings for a specific sensor within a time range. Which AWS database is most cost-effective?
easyWhy A: Option B is correct because Timestream is purpose-built for time-series data with automatic tiering. Option A is wrong because DynamoDB is more expensive for this pattern. Option C is wrong because RDS is not optimized for time-series. Option D is wrong because Redshift is for analytics, not real-time ingestion.
Last reviewed: Jun 24, 2026
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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