- A
Amazon S3 with a partition prefix of device_id/timestamp/
Why wrong: S3 has eventual consistency and higher latency per PUT; not ideal for high-rate small writes.
- B
Amazon Redshift with distribution key on device_id
Why wrong: Redshift is built for analytical queries on large datasets, not for real-time point writes.
- C
Amazon DynamoDB with a composite primary key (device_id, timestamp)
DynamoDB provides low-latency, high-throughput ingestion and efficient querying by device and time.
- D
Amazon RDS for MySQL with multiple read replicas
Why wrong: RDS is relational and not optimized for high-velocity ingestion of small documents; scaling is limited.
Question 992 of 1,730
IoT Time-Series Data Modeling with DynamoDB Composite Key for Range Queries
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 is a small JSON document (less than 1 KB) and must be stored with low latency. Queries are primarily by device ID and timestamp range. The team expects to rarely update or delete old data. Which AWS database solution is MOST cost-effective and performant?
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 a composite primary key (device_id, timestamp)
Amazon DynamoDB with a composite primary key (device_id, timestamp) is the most cost-effective and performant solution because it provides single-digit millisecond latency for point lookups and range queries, scales horizontally to handle millions of writes per second, and its on-demand or auto-scaling capacity model avoids over-provisioning. The access pattern of querying by device ID and timestamp range maps directly to DynamoDB's partition and sort key design, enabling efficient use of the Query API without scanning.
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 S3 with a partition prefix of device_id/timestamp/
Why it's wrong here
S3 has eventual consistency and higher latency per PUT; not ideal for high-rate small writes.
- ✗
Amazon Redshift with distribution key on device_id
Why it's wrong here
Redshift is built for analytical queries on large datasets, not for real-time point writes.
- ✓
Amazon DynamoDB with a composite primary key (device_id, timestamp)
Why this is correct
DynamoDB provides low-latency, high-throughput ingestion and efficient querying by device and time.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Amazon RDS for MySQL with multiple read replicas
Why it's wrong here
RDS is relational and not optimized for high-velocity ingestion of small documents; scaling is limited.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The DBS-C01 exam often tests the misconception that S3 is suitable for low-latency, high-write IoT ingestion, but the trap here is that S3's eventual consistency and higher per-request latency make it inappropriate for real-time sensor data storage, whereas DynamoDB's design for exactly this pattern is the correct choice.
Detailed technical explanation
How to think about this question
DynamoDB's partition key (device_id) distributes data across partitions for write scalability, while the sort key (timestamp) enables efficient range queries via the Query API using the BETWEEN operator. Under the hood, DynamoDB uses consistent hashing to route requests, and the adaptive capacity feature automatically rebalances hot partitions. In a real-world IoT scenario, you can further optimize by using TTL to automatically expire old sensor data, reducing storage costs without application-level cleanup.
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.
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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 a composite primary key (device_id, timestamp) — Amazon DynamoDB with a composite primary key (device_id, timestamp) is the most cost-effective and performant solution because it provides single-digit millisecond latency for point lookups and range queries, scales horizontally to handle millions of writes per second, and its on-demand or auto-scaling capacity model avoids over-provisioning. The access pattern of querying by device ID and timestamp range maps directly to DynamoDB's partition and sort key design, enabling efficient use of the Query API without scanning.
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
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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