Quick Answer
The answer is Amazon Timestream and Amazon Redshift with streaming ingestion from Kinesis. Amazon Timestream is purpose-built for time-series data, using a dedicated query engine and automatic tiering between in-memory and magnetic stores to deliver sub-second query latency on streaming data. Amazon Redshift, when configured with streaming ingestion from Kinesis, directly consumes data streams into materialized views, enabling real-time analytics without batch loading. On the AWS Certified Database Specialty DBS-C01 exam, this question tests your understanding of which services are optimized for low-latency, continuous data ingestion versus those designed for batch or OLTP workloads. A common trap is choosing Amazon RDS or DynamoDB, which lack native streaming ingestion and sub-second analytical query performance. Remember the memory tip: “Timestream for time, Redshift for streams” — Timestream handles time-series natively, while Redshift streams from Kinesis for real-time SQL analytics.
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 choose a database for a real-time analytics workload that requires sub-second query latency on streaming data. Which TWO AWS services are most suitable?
Amazon Neptune.
Why wrong: Graph database.
Amazon RDS for PostgreSQL with materialized views.
Why wrong: Not optimized for streaming.
Amazon Redshift with streaming ingestion from Kinesis.
Supports near-real-time analytics.
Amazon Timestream.
Purpose-built for time-series with sub-second queries.
Amazon DynamoDB Accelerator (DAX).
Why wrong: Caching layer, not analytics.
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 Redshift with streaming ingestion from Kinesis.
Amazon Timestream is purpose-built for time-series data and provides sub-second query latency on streaming data via its dedicated query engine and automatic tiering between in-memory and magnetic stores. Amazon Redshift with streaming ingestion from Kinesis enables real-time analytics by directly consuming Kinesis data streams into Redshift materialized views, allowing sub-second queries on fresh data without batch loading.
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 Neptune.
Why it's wrong here
Graph database.
Amazon RDS for PostgreSQL with materialized views.
Why it's wrong here
Not optimized for streaming.
Amazon Redshift with streaming ingestion from Kinesis.
Why this is correct
Supports near-real-time analytics.
Related concept
Read the scenario before looking for a memorised answer.
Amazon Timestream.
Why this is correct
Purpose-built for time-series with sub-second queries.
Related concept
Read the scenario before looking for a memorised answer.
Amazon DynamoDB Accelerator (DAX).
Why it's wrong here
Caching layer, not analytics.
Common exam traps
The trap here is that candidates often confuse low-latency caching services like DAX or traditional databases with materialized views as suitable for real-time streaming analytics, overlooking that only purpose-built time-series databases or services with native streaming ingestion can guarantee sub-second query latency on continuous data streams.
Detailed technical explanation
Amazon Timestream uses a purpose-built query engine that automatically routes recent data from the in-memory store and historical data from the magnetic store, enabling sub-second queries on streaming data without manual partitioning. Amazon Redshift streaming ingestion leverages materialized views that incrementally refresh from Kinesis Data Streams using a native connector, avoiding the latency of staging data in S3 and allowing sub-second query performance on fresh data through result caching and automatic query optimization. In real-world scenarios, a company processing IoT sensor data can use Timestream for time-series-specific queries (e.g., aggregation over windows) or Redshift for complex SQL analytics combining streaming data with historical tables.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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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 Redshift with streaming ingestion from Kinesis. — Amazon Timestream is purpose-built for time-series data and provides sub-second query latency on streaming data via its dedicated query engine and automatic tiering between in-memory and magnetic stores. Amazon Redshift with streaming ingestion from Kinesis enables real-time analytics by directly consuming Kinesis data streams into Redshift materialized views, allowing sub-second queries on fresh data without batch loading.
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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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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