DBS-C01 Workload-Specific Database Design Practice Question
A gaming company uses Amazon DynamoDB with global tables across two regions. They notice increased write latency and throttling during peak hours. The access pattern is mostly writes to a small set of hot partitions. Which design change would best address this?
⚠ Common exam trap
Test-takers frequently confuse throughput scaling (options C and D) with partition-level distribution, failing to recognize that hot partitions require a key design change, not just capacity adjustments.
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 write sharding using a random suffix on the partition key
The issue is hot partitions caused by a small set of partition keys receiving the majority of writes. By implementing write sharding with a random suffix on the partition key, you distribute writes across multiple partitions, reducing throttling and write latency. This directly addresses the root cause of uneven access patterns, unlike the other options that either cache reads, adjust capacity mode, or scale capacity without solving the partition-level bottleneck.
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 write sharding using a random suffix on the partition key
Why this is correct
Write sharding distributes writes evenly across partitions.
- ✗
Enable DynamoDB Accelerator (DAX)
Why it's wrong here
DAX is a read cache, does not reduce write hot spots.
- ✗
Switch to DynamoDB on-demand capacity mode
Why it's wrong here
On-demand helps with unpredictable traffic but not hot keys.
- ✗
Increase write capacity using auto scaling
Why it's wrong here
Auto scaling adds capacity but hot partition still throttles.
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