The answer is to use DynamoDB Accelerator (DAX) and adaptive capacity. DAX optimizes read performance for IoT data by providing an in-memory cache that serves the most recent reads for a specific device, drastically reducing read latency and cutting costs by lowering read capacity unit consumption on repeated queries. Adaptive capacity, meanwhile, automatically redistributes traffic across partitions when a single device_id receives a high volume of writes, preventing hot partitions and ensuring consistent performance without manual intervention. On the AWS Certified Database Specialty DBS-C01 exam, this question tests your understanding of DynamoDB’s caching and workload management features, often appearing as a paired design pattern for high-write, read-recent workloads. A common trap is to overlook adaptive capacity and instead suggest manual partition splitting or global tables, which add cost and complexity. Remember the mnemonic “DAX for reads, adaptive for heat” to recall that DAX caches hot read data while adaptive capacity handles hot write partitions.
DBS-C01 Workload-Specific Database Design Practice Question
This DBS-C01 practice question tests your understanding of workload-specific database design. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 using Amazon DynamoDB to store IoT sensor data. The application writes a large volume of data and needs to read recent data by timestamp. The table has a partition key of device_id and a sort key of timestamp. The access pattern is to read the latest data for a specific device. Which TWO design patterns will optimize read performance and reduce costs?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use adaptive capacity to evenly distribute traffic across partitions.
Option A is correct because adaptive capacity allows DynamoDB to automatically manage partition traffic distribution, preventing hot partitions when a single device_id receives a high volume of writes. This ensures consistent read performance without manual partition management. Option B is correct because DAX provides an in-memory cache for the most frequently accessed data, reducing read latency and read capacity unit consumption for repeated queries of recent sensor data.
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.
✓
Use adaptive capacity to evenly distribute traffic across partitions.
Why this is correct
Adaptive capacity helps handle hot partitions, improving performance and cost efficiency.
Related concept
Read the scenario before looking for a memorised answer.
✓
Use DynamoDB Accelerator (DAX) to cache the most recent reads.
Why this is correct
DAX caches reads, reducing read capacity units consumed.
Related concept
Read the scenario before looking for a memorised answer.
✗
Enable auto scaling for the table to handle spikes.
Why it's wrong here
Auto scaling manages capacity but does not reduce costs or optimize read performance directly.
✗
Use DynamoDB Transactions for consistent reads.
Why it's wrong here
Transactions consume more capacity and are not needed for this pattern.
✗
Create a global secondary index (GSI) with device_id as partition key and timestamp as sort key.
Why it's wrong here
This duplicates the base table; no performance gain.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse auto scaling with adaptive capacity, or assume a GSI is always beneficial, not realizing that duplicating the base table key structure adds cost without performance gain.
Detailed technical explanation
How to think about this question
Adaptive capacity works by splitting partitions that experience high traffic into smaller sub-partitions, distributing I/O across more resources without application changes. DAX uses a write-through cache that maintains strong consistency for cached items, reducing the number of reads that hit the underlying table's provisioned capacity. In practice, combining DAX with a TTL-based expiration for stale data can further reduce costs by caching only the most recent timestamps per device.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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: Use adaptive capacity to evenly distribute traffic across partitions. — Option A is correct because adaptive capacity allows DynamoDB to automatically manage partition traffic distribution, preventing hot partitions when a single device_id receives a high volume of writes. This ensures consistent read performance without manual partition management. Option B is correct because DAX provides an in-memory cache for the most frequently accessed data, reducing read latency and read capacity unit consumption for repeated queries of recent sensor data.
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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Question Discussion
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