DBS-C01 Workload-Specific Database Design Practice Question
A company uses Amazon DynamoDB for a session management store. The application writes and reads session data frequently. The team notices that write requests occasionally fail with ProvisionedThroughputExceededException. They want a cost-effective solution to handle these bursts. What should they do?
⚠ Common exam trap
It's easy for candidates to confuse DAX (a read cache) with a write buffer, or assume that a higher fixed capacity is the only way to handle bursts, ignoring the cost-efficiency requirement that points to Auto Scaling.
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
✓
Enable DynamoDB Auto Scaling for the table
DynamoDB Auto Scaling dynamically adjusts the provisioned write capacity based on actual traffic patterns, handling bursts cost-effectively by scaling up during spikes and down during lulls. This avoids the fixed-cost overhead of a higher provisioned value (Option A) and directly addresses the ProvisionedThroughputExceededException by ensuring sufficient capacity during bursts.
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 provisioned write capacity to a higher fixed value
Why it's wrong here
This would be more expensive and may still not handle extreme bursts.
- ✗
Use DynamoDB Accelerator (DAX) to cache writes
Why it's wrong here
DAX is a read cache, does not help with write throughput.
- ✗
Implement an Amazon SQS queue to buffer writes
Why it's wrong here
Buffering writes adds latency and complexity; not a direct solution for throughput.
- ✓
Enable DynamoDB Auto Scaling for the table
Why this is correct
Auto Scaling adjusts capacity based on actual usage, handling bursts cost-effectively.
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