- A
Amazon Redshift with auto-copy from S3
Why wrong: Redshift is for analytics, not real-time ingestion of millions per second.
- B
Amazon ElastiCache for Redis with time-series module
Why wrong: ElastiCache is a cache, not a durable data store for 90-day retention.
- C
Amazon Timestream
Timestream is purpose-built for time-series data, handles high ingestion, and includes built-in analytics.
- D
Amazon DynamoDB with TTL
Why wrong: DynamoDB is not optimized for time-series analytics and may be expensive for high write throughput.
Question 444 of 1,730
IoT Time-Series Analytics with Amazon Timestream
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 is designing a database for an IoT application that ingests millions of sensor readings per second. Each reading includes device ID, timestamp, and measurement. The workload requires time-series analytics and data retention for 90 days. Which AWS database solution 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 a purpose-built time-series database designed for IoT and operational applications that ingest millions of sensor readings per second. It automatically manages data retention policies (e.g., 90 days) by storing recent data in memory and historical data in a cost-optimized store, and it supports time-series analytics with built-in functions like interpolation and smoothing.
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 Redshift with auto-copy from S3
Why it's wrong here
Redshift is for analytics, not real-time ingestion of millions per second.
- ✗
Amazon ElastiCache for Redis with time-series module
Why it's wrong here
ElastiCache is a cache, not a durable data store for 90-day retention.
- ✓
Amazon Timestream
Why this is correct
Timestream is purpose-built for time-series data, handles high ingestion, and includes built-in analytics.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Amazon DynamoDB with TTL
Why it's wrong here
DynamoDB is not optimized for time-series analytics and may be expensive for high write throughput.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose DynamoDB with TTL because they associate it with time-series data and automatic expiration, but they overlook the lack of native time-series analytics and the performance challenges of range queries across high-cardinality device IDs.
Detailed technical explanation
How to think about this question
Timestream uses a two-tier storage model: a memory store for recent data (configurable up to 24 hours) and a magnetic store for historical data, with automatic data movement based on retention policies. It supports SQL-compatible queries with time-series-specific functions like `DATE_BIN`, `INTERPOLATE_LINEAR`, and `SMOOTH`, enabling real-time and historical analytics without manual partitioning. In a real-world IoT scenario with 10 million devices, Timestream can ingest over 1 million writes per second while maintaining query performance for 90-day retention, whereas DynamoDB would require manual sharding and TTL-based cleanup that cannot guarantee consistent query latency.
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.
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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 a purpose-built time-series database designed for IoT and operational applications that ingest millions of sensor readings per second. It automatically manages data retention policies (e.g., 90 days) by storing recent data in memory and historical data in a cost-optimized store, and it supports time-series analytics with built-in functions like interpolation and smoothing.
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 →
Same concept, more angles
4 more ways this is tested on DBS-C01
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 ingests millions of sensor readings per second. The data is time-series and is queried to generate reports on average temperature over the last hour. Which TWO database solutions are most suitable for this workload?
medium- ✓ A.Amazon DynamoDB with time-series data model and TTL
- B.Amazon Neptune
- C.Amazon RDS for PostgreSQL
- ✓ D.Amazon Timestream
- E.Amazon ElastiCache for Redis
Why A: Amazon DynamoDB with a time-series data model and TTL is suitable because it can handle high-throughput writes from millions of sensors per second using partition key design (e.g., sensor ID + timestamp) and automatically expire old data via TTL, reducing storage costs. The query pattern for average temperature over the last hour can be efficiently served using DynamoDB's built-in time-series optimizations, such as adaptive capacity and on-demand scaling, without the overhead of relational joins. Amazon Timestream is also suitable because it is a purpose-built time-series database that can ingest millions of sensor readings per second and provides built-in functions for time-series aggregation, such as average temperature over the last hour, with automatic data retention policies and tiered storage for cost optimization.
Variation 2. A company is designing a database for an IoT application that ingests millions of small sensor readings per second. The data is time-series and queries are mostly range scans over time. The company needs a cost-effective solution with high write throughput. Which AWS service should the database specialist recommend?
easy- A.Amazon Redshift with auto-ingest
- ✓ B.Amazon Timestream
- C.Amazon RDS for PostgreSQL with pg_partman extension
- D.Amazon DynamoDB with time-series data modeling
Why B: Amazon Timestream is a purpose-built time-series database designed for IoT and operational applications that ingest millions of data points per second. It automatically manages storage tiers (in-memory and magnetic) to optimize cost, and its query engine is optimized for range scans over time, making it the most cost-effective and high-throughput choice for this workload.
Variation 3. A company is designing a database for an IoT application that ingests millions of small sensor readings per second. The data is append-only and queries are primarily time-based aggregations with low latency requirements (under 10 ms). Which AWS database service is most suitable for this workload?
medium- A.Amazon DynamoDB
- B.Amazon ElastiCache
- C.Amazon Aurora
- ✓ D.Amazon Timestream
Why D: Amazon Timestream is a purpose-built time-series database designed for IoT and operational applications that ingest high volumes of append-only data. It automatically manages storage tiers (in-memory and magnetic) and provides built-in time-based aggregation functions, enabling queries with sub-10 ms latency for recent data. This makes it the most suitable choice for the described workload of millions of sensor readings per second with low-latency aggregation queries.
Variation 4. A company is designing a database for an IoT application that ingests millions of time-series data points per second. The database must support high-throughput writes and efficient querying of recent data. Which AWS database service is MOST suitable?
easy- A.Amazon RDS for PostgreSQL
- ✓ B.Amazon Timestream
- C.Amazon DynamoDB with TTL
- D.Amazon Redshift
Why B: Amazon Timestream is purpose-built for time-series data, offering a serverless architecture that ingests millions of data points per second with automatic scaling. It provides efficient storage and querying of recent data through its memory store, while tiering older data to a cost-optimized magnetic store, making it the most suitable choice for high-throughput IoT time-series workloads.
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Last reviewed: Jun 24, 2026
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