- A
Use Amazon Kinesis Data Analytics to analyze data in real-time and store results in S3.
Kinesis Data Analytics can run SQL on streaming data and store results.
- B
Use Amazon RDS for PostgreSQL with TimescaleDB extension.
Why wrong: Not as scalable as Timestream for IoT scale.
- C
Use Amazon Redshift with automated snapshot retention of 90 days.
Why wrong: Redshift is not low-latency for recent data without streaming.
- D
Use Amazon Timestream for storing time-series data and run SQL queries.
Timestream is purpose-built for time-series and supports SQL.
- E
Use Amazon DynamoDB with TTL to expire data after 90 days and use PartiQL for queries.
Why wrong: PartiQL is limited; DynamoDB is not optimal for time-series analytics.
Question 433 of 1,746
SAP-C02 Amazon Timestream Practice Question
This SAP-C02 practice question tests your understanding of design for new solutions. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. A key principle to apply: amazon Timestream. 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 new solution to store and analyze large volumes of IoT sensor data. The data is time-series and must be retained for 90 days. The company needs to run complex SQL queries on the data and expects low latency for the most recent 7 days of data. Which TWO solutions meet these requirements? (Choose TWO.)
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
✓
Use Amazon Kinesis Data Analytics to analyze data in real-time and store results in S3.
Option A uses Amazon Kinesis Data Analytics to perform real-time SQL analytics on streaming IoT sensor data, providing low-latency access to the most recent 7 days of data. The analyzed results can be stored in Amazon S3, which can retain data for 90 days, meeting the retention requirement. Option D uses Amazon Timestream, a purpose-built time-series database that automatically stores recent data in memory for low-latency queries and moves older data to a cost-optimized storage tier, with a default retention period configurable up to 90 days. Timestream supports standard SQL queries. Option B (RDS with TimescaleDB) is not serverless and requires manual scaling; it also does not natively integrate with streaming data at scale. Option C (Redshift) is not optimized for real-time ingestion or low-latency queries on recent data; its snapshot retention is for backup, not for time-series query performance. Option E (DynamoDB with TTL and PartiQL) does not efficiently support time-series analytics or complex SQL queries on large volumes of sensor data; PartiQL has limitations for analytical queries.
Key principle: Amazon Timestream
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use Amazon Kinesis Data Analytics to analyze data in real-time and store results in S3.
Why this is correct
Kinesis Data Analytics can run SQL on streaming data and store results.
Related concept
Amazon Timestream
- ✗
Use Amazon RDS for PostgreSQL with TimescaleDB extension.
Why it's wrong here
Not as scalable as Timestream for IoT scale.
- ✗
Use Amazon Redshift with automated snapshot retention of 90 days.
Why it's wrong here
Redshift is not low-latency for recent data without streaming.
- ✓
Use Amazon Timestream for storing time-series data and run SQL queries.
Why this is correct
Timestream is purpose-built for time-series and supports SQL.
Related concept
Amazon Timestream
- ✗
Use Amazon DynamoDB with TTL to expire data after 90 days and use PartiQL for queries.
Why it's wrong here
PartiQL is limited; DynamoDB is not optimal for time-series analytics.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates may incorrectly select Amazon Redshift (option C) due to its snapshot retention feature, but Redshift is not optimized for real-time ingestion or low-latency queries on recent data. Similarly, Amazon DynamoDB (option E) is not ideal for time-series queries despite TTL and PartiQL. Amazon RDS with TimescaleDB (option B) could work but is not a fully managed AWS service for time-series. The correct choices are the two AWS-native services: Kinesis Data Analytics and Timestream.
Detailed technical explanation
How to think about this question
Amazon Timestream is a purpose-built time-series database that automatically manages storage tiers (memory for recent data and magnetic store for historical data), enabling low-latency queries on the most recent 7 days of data while retaining data for 90 days. Kinesis Data Analytics uses Apache Flink under the hood to perform real-time SQL-based analytics on streaming data, and the results can be persisted in S3 for cost-effective long-term storage, with S3 Lifecycle policies to manage the 90-day retention.
KKey Concepts to Remember
- Amazon Timestream
- Amazon Kinesis Data Analytics
- Time-series data management
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
Amazon Timestream
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.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
What to study next
Got this wrong? Here's your next step.
Review amazon Timestream, then practise related SAP-C02 questions on the same topic to reinforce the concept.
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FAQ
Questions learners often ask
What does this SAP-C02 question test?
Design for New Solutions — This question tests Design for New Solutions — Amazon Timestream.
What is the correct answer to this question?
The correct answer is: Use Amazon Kinesis Data Analytics to analyze data in real-time and store results in S3. — Option A uses Amazon Kinesis Data Analytics to perform real-time SQL analytics on streaming IoT sensor data, providing low-latency access to the most recent 7 days of data. The analyzed results can be stored in Amazon S3, which can retain data for 90 days, meeting the retention requirement. Option D uses Amazon Timestream, a purpose-built time-series database that automatically stores recent data in memory for low-latency queries and moves older data to a cost-optimized storage tier, with a default retention period configurable up to 90 days. Timestream supports standard SQL queries. Option B (RDS with TimescaleDB) is not serverless and requires manual scaling; it also does not natively integrate with streaming data at scale. Option C (Redshift) is not optimized for real-time ingestion or low-latency queries on recent data; its snapshot retention is for backup, not for time-series query performance. Option E (DynamoDB with TTL and PartiQL) does not efficiently support time-series analytics or complex SQL queries on large volumes of sensor data; PartiQL has limitations for analytical queries.
What should I do if I get this SAP-C02 question wrong?
Review amazon Timestream, then practise related SAP-C02 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Amazon Timestream
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Last reviewed: Jul 4, 2026
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