- A
Amazon ElastiCache for Redis for real-time queries, and Amazon OpenSearch Service for historical analytics.
Why wrong: ElastiCache is not designed for 10,000 writes per second of 1 KB events, and OpenSearch is not cost-effective for ad-hoc analytical queries on months of data.
- B
Amazon DynamoDB with DynamoDB Accelerator (DAX) for real-time queries, and Amazon S3 with Amazon Athena for historical analytics.
DynamoDB handles high write throughput, DAX provides sub-second reads, and S3 with Athena allows cost-effective ad-hoc queries on historical data.
- C
Amazon Redshift for both real-time and historical queries, using auto-scaling and materialized views.
Why wrong: Redshift is optimized for complex analytical queries but not for sub-second real-time queries on fast-changing data.
- D
Amazon RDS for PostgreSQL with read replicas for real-time queries, and Amazon Redshift for historical analytics.
Why wrong: RDS PostgreSQL cannot handle 10,000 writes per second, and read replicas add latency. Redshift is not needed if S3+Athena suffices.
DBS-C01 Workload-Specific Database Design Practice Question
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 startup is building a real-time analytics dashboard on AWS. The data arrives as time-series events from IoT devices at a rate of 10,000 writes per second. Each event is approximately 1 KB. The dashboard requires sub-second query latency for the last hour of data and must support ad-hoc analytical queries on historical data spanning months. The team needs to design a cost-effective database solution. Which combination of AWS services should be used?
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 DynamoDB with DynamoDB Accelerator (DAX) for real-time queries, and Amazon S3 with Amazon Athena for historical analytics.
Option B is correct because DynamoDB with DAX provides microsecond to sub-millisecond latency for real-time queries on the last hour of data, while S3 with Athena offers a cost-effective serverless solution for ad-hoc analytical queries on historical data spanning months. DynamoDB's time-to-live (TTL) feature can automatically expire data older than one hour, keeping the hot dataset small and performant, and Athena's pay-per-query pricing avoids the cost of maintaining a separate analytics cluster.
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 ElastiCache for Redis for real-time queries, and Amazon OpenSearch Service for historical analytics.
Why it's wrong here
ElastiCache is not designed for 10,000 writes per second of 1 KB events, and OpenSearch is not cost-effective for ad-hoc analytical queries on months of data.
- ✓
Amazon DynamoDB with DynamoDB Accelerator (DAX) for real-time queries, and Amazon S3 with Amazon Athena for historical analytics.
Why this is correct
DynamoDB handles high write throughput, DAX provides sub-second reads, and S3 with Athena allows cost-effective ad-hoc queries on historical data.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Amazon Redshift for both real-time and historical queries, using auto-scaling and materialized views.
Why it's wrong here
Redshift is optimized for complex analytical queries but not for sub-second real-time queries on fast-changing data.
- ✗
Amazon RDS for PostgreSQL with read replicas for real-time queries, and Amazon Redshift for historical analytics.
Why it's wrong here
RDS PostgreSQL cannot handle 10,000 writes per second, and read replicas add latency. Redshift is not needed if S3+Athena suffices.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose ElastiCache or Redshift for real-time performance, overlooking that DynamoDB with DAX is purpose-built for high-throughput, low-latency key-value access and that S3 with Athena is the most cost-effective serverless option for infrequent analytical queries on large historical datasets.
Detailed technical explanation
How to think about this question
DynamoDB's adaptive capacity automatically partitions hot keys to handle high write throughput, and DAX acts as a write-through cache that reduces read latency to microseconds for frequently accessed items. Athena uses Presto under the hood to query data directly from S3, and partitioning data by month or day with Parquet/ORC formats can reduce scan costs by over 90% for historical queries. The combination leverages DynamoDB's TTL to automatically move expired data to S3 via DynamoDB Streams and AWS Lambda, creating a seamless hot-warm-cold tier without manual intervention.
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.
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.
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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 DynamoDB with DynamoDB Accelerator (DAX) for real-time queries, and Amazon S3 with Amazon Athena for historical analytics. — Option B is correct because DynamoDB with DAX provides microsecond to sub-millisecond latency for real-time queries on the last hour of data, while S3 with Athena offers a cost-effective serverless solution for ad-hoc analytical queries on historical data spanning months. DynamoDB's time-to-live (TTL) feature can automatically expire data older than one hour, keeping the hot dataset small and performant, and Athena's pay-per-query pricing avoids the cost of maintaining a separate analytics cluster.
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.
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Last reviewed: Jul 4, 2026
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