DBS-C01 Management and Operations Practice Question
A company is using Amazon DynamoDB with on-demand capacity mode. The application experiences occasional throttling during peak hours. The operations team wants to reduce throttling without changing the application code. What should they do?
⚠ Common exam trap
Watch out — candidates often assume on-demand mode never throttles, but it can throttle during sudden traffic spikes that exceed the previous 30-minute peak, and they mistakenly think increasing capacity units is possible in on-demand mode.
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
✓
Switch to provisioned capacity mode and configure auto scaling.
DynamoDB on-demand capacity mode does not allow manual adjustment of capacity units; it scales automatically but can still throttle if traffic exceeds the previous peak by a large margin. Switching to provisioned capacity mode with auto scaling allows you to set a higher minimum capacity and scale proactively based on actual usage patterns, reducing throttling without code changes. This approach gives more control over capacity limits while still automating adjustments.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the read and write capacity units in the on-demand mode.
Why it's wrong here
On-demand mode does not allow direct capacity adjustment.
- ✓
Switch to provisioned capacity mode and configure auto scaling.
Why this is correct
Provisioned capacity with auto scaling ensures adequate capacity for peak traffic.
- ✗
Enable DynamoDB Accelerator (DAX) to cache reads.
Why it's wrong here
DAX helps with read performance, not write throttling.
- ✗
Use DynamoDB global tables to distribute traffic across regions.
Why it's wrong here
Global tables do not affect capacity limits per table.
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Senior Network & Security Engineer · founder of Courseiva
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