DBS-C01 Management and Operations Practice Question
Which TWO of the following are valid strategies for reducing costs for an Amazon DynamoDB table with on-demand capacity mode? (Choose TWO.)
⚠ Common exam trap
It's easy for candidates to 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.
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
✓
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.
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.
- ✗
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.
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