The answer is switching to provisioned capacity mode with auto scaling and implementing DAX. Switching to provisioned capacity with auto scaling is more cost-effective for reducing DynamoDB on-demand capacity costs because on-demand pricing charges per request, which becomes expensive for predictable or steady workloads, whereas provisioned capacity lets you pay a fixed baseline for read and write units, with auto scaling adjusting to traffic spikes. Implementing DAX, a fully managed in-memory cache, reduces the number of read requests hitting the underlying table by serving cached results, directly lowering read capacity consumption and thus costs for on-demand tables. On the AWS Certified Database Specialty DBS-C01 exam, this tests your understanding of DynamoDB capacity modes and caching strategies, often appearing as a trap where candidates mistakenly choose options like deleting unused indexes or reducing item sizes—while helpful, these are not the primary cost-reduction strategies for on-demand mode. A useful memory tip: think “DAX for reads, provisioned for steady needs” to recall the two valid approaches.
DBS-C01 Management and Operations Practice Question
This DBS-C01 practice question tests your understanding of management and operations. 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.
Which TWO of the following are valid strategies for reducing costs for an Amazon DynamoDB table with on-demand capacity mode? (Choose TWO.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Implement DynamoDB Accelerator (DAX) to cache reads.
Implementing DAX reduces the number of read requests to the underlying DynamoDB table by serving cached results, which lowers read capacity consumption and thus reduces costs for on-demand tables. Switching to provisioned capacity with auto scaling allows you to pay for a baseline of read/write capacity units rather than per-request pricing, which is more cost-effective for predictable or steady workloads.
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.
✓
Implement DynamoDB Accelerator (DAX) to cache reads.
Why this is correct
DAX reduces the number of read requests to DynamoDB, lowering read capacity consumption.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use Amazon DynamoDB TTL to automatically delete expired data.
Why it's wrong here
TTL removes data but does not directly reduce capacity costs; it may reduce storage costs.
✗
Enable DynamoDB global tables to distribute traffic.
Why it's wrong here
Global tables increase costs due to replication.
✗
Use DynamoDB Streams to process updates asynchronously.
Why it's wrong here
Streams do not directly reduce capacity costs.
✓
Switch to provisioned capacity mode with auto scaling.
Why this is correct
Provisioned capacity with auto scaling can be more cost-effective for predictable workloads.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often think TTL or Streams reduce operational costs, but they only affect storage or enable event-driven processing, not the per-request billing that drives on-demand costs.
Detailed technical explanation
How to think about this question
DAX is an in-memory cache that offloads read traffic from the DynamoDB table, reducing the number of eventually consistent and strongly consistent read requests billed per million. Provisioned capacity with auto scaling adjusts capacity based on actual usage patterns, allowing you to avoid the per-request premium of on-demand mode while still handling traffic spikes within the auto scaling limits. The cost difference can be significant: on-demand charges per million read/write request units, whereas provisioned capacity charges per hour for allocated RCUs and WCUs, making provisioned mode cheaper for steady or predictable workloads.
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.
Management and Operations — This question tests Management and Operations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Implement DynamoDB Accelerator (DAX) to cache reads. — Implementing DAX reduces the number of read requests to the underlying DynamoDB table by serving cached results, which lowers read capacity consumption and thus reduces costs for on-demand tables. Switching to provisioned capacity with auto scaling allows you to pay for a baseline of read/write capacity units rather than per-request pricing, which is more cost-effective for predictable or steady workloads.
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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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.
Question Discussion
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