Quick Answer
The correct answer is Amazon Timestream, Amazon DynamoDB, and Amazon OpenSearch Service. Amazon Timestream is purpose-built for time-series workloads, offering automatic write optimization and built-in time-based aggregation functions that handle high-frequency writes at millions per second without manual tuning. DynamoDB supports this pattern through careful time-series partitioning—using the entity ID as the partition key and timestamp as the sort key—which distributes writes evenly and enables efficient range queries over time windows. OpenSearch Service ingests streaming time-series data and provides powerful aggregation queries via its date histogram and pipeline aggregations. On the AWS Certified Database Specialty DBS-C01 exam, this question tests your ability to distinguish purpose-built services from general-purpose databases; a common trap is choosing Amazon Redshift or Aurora, which lack the write throughput and time-series optimizations required here. Memory tip: think “TDO” for Timestream, DynamoDB, OpenSearch—the three services that together handle time-series ingestion, storage, and 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 is moving a large-scale time-series application from Cassandra to a managed AWS service. The workload involves high-frequency writes (millions per second) and queries that aggregate data over time windows. Which THREE AWS services are suitable for this time-series workload?
Amazon OpenSearch Service
Supports time-series ingestion and aggregation.
Amazon Aurora
Why wrong: Relational database with write limitations.
Amazon DynamoDB
Can handle high write throughput with proper time-series pattern.
Amazon Timestream
Designed for time-series data with built-in aggregation.
Amazon Redshift
Why wrong: Not optimized for high-frequency writes.
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 OpenSearch Service
Amazon Timestream is purpose-built for time-series data. Amazon DynamoDB with time-series partitioning (using partition key as entity ID and sort key as timestamp) can handle high-volume writes and support time-based queries. Amazon OpenSearch Service can ingest time-series data and provide aggregation queries. Option D (Redshift) is wrong because it is optimized for OLAP on structured data, not high-frequency writes. Option E (Aurora) is wrong because it is not designed for time-series ingestion at millions of writes per second.
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 OpenSearch Service
Why this is correct
Supports time-series ingestion and aggregation.
Related concept
Read the scenario before looking for a memorised answer.
Amazon Aurora
Why it's wrong here
Relational database with write limitations.
Amazon DynamoDB
Why this is correct
Can handle high write throughput with proper time-series pattern.
Related concept
Read the scenario before looking for a memorised answer.
Amazon Timestream
Why this is correct
Designed for time-series data with built-in aggregation.
Related concept
Read the scenario before looking for a memorised answer.
Amazon Redshift
Why it's wrong here
Not optimized for high-frequency writes.
Common exam traps
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
Identify which DBS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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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 OpenSearch Service — Amazon Timestream is purpose-built for time-series data. Amazon DynamoDB with time-series partitioning (using partition key as entity ID and sort key as timestamp) can handle high-volume writes and support time-based queries. Amazon OpenSearch Service can ingest time-series data and provide aggregation queries. Option D (Redshift) is wrong because it is optimized for OLAP on structured data, not high-frequency writes. Option E (Aurora) is wrong because it is not designed for time-series ingestion at millions of writes per second.
Identify which DBS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Read the scenario before looking for a memorised answer.
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Same concept, more angles
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Variation 1. A company runs a time-series application that records sensor data every second. The data volume is 500 GB per month and grows continuously. They need to query the last 30 days of data frequently and older data rarely. Which database design is MOST appropriate?
easyWhy A: Amazon Timestream is purpose-built for time-series data, automatically storing recent data in memory for fast queries and moving older data to a cost-optimized store. This matches the workload of frequent queries on the last 30 days and rare queries on older data, with continuous growth at 500 GB/month.
Last reviewed: Jun 20, 2026
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