DBS-C01 Hot partition Practice Question
A company is using Amazon DynamoDB for a gaming leaderboard. The table has a partition key of 'game_id' and a sort key of 'score'. The table is configured with on-demand capacity. During a major tournament, the application experiences high latency and some requests return 'ProvisionedThroughputExceededException' errors. The CloudWatch metric 'ThrottledRequests' spikes. The application uses a single partition key for all writes during the tournament (game_id = 'tournament_final'). What is the most likely cause of the throttling, and what is the best solution?
⚠ Common exam trap
The trap is assuming that on-demand capacity eliminates all throttling. In reality, on-demand capacity has per-partition limits (up to 1,000 write capacity units per partition) that can still be exceeded if a single partition receives all traffic.
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
✓
The application is using a single partition key, causing all writes to go to one partition. The team should redesign the partition key to distribute writes across multiple partitions
Even with on-demand capacity, each partition has a maximum throughput limit (read/write capacity per partition). When all writes use the same partition key ('tournament_final'), they all go to a single partition, exceeding its limit and causing throttling (ThrottledRequests and ProvisionedThroughputExceededException). The best solution is to redesign the partition key to distribute writes across multiple partitions (e.g., add a suffix like user_id or timestamp). Option B is incorrect because DAX is a caching layer for reads, not writes; it does not alleviate write throttling. Option C is incorrect because the question does not mention a GSI, and a GSI would not cause this issue on the base table. Option D is incorrect because switching to provisioned capacity does not solve the hot partition problem; the same partition key would still overload a single partition under provisioned capacity.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
The application is using a single partition key, causing all writes to go to one partition. The team should redesign the partition key to distribute writes across multiple partitions
Why this is correct
Distributing the write load across partitions avoids throttling.
- ✗
The application is using a single partition key, causing all writes to go to one partition. The team should implement DAX to cache writes
Why it's wrong here
DAX does not cache writes; it caches reads.
- ✗
The table has a global secondary index that is throttling writes; the team should remove the GSI
Why it's wrong here
The primary table is throttling, not the GSI.
- ✗
The table is using on-demand capacity, which has a maximum throughput limit per partition; the team should switch to provisioned capacity with auto scaling
Why it's wrong here
On-demand has per-partition limits; switching to provisioned may not solve the hot partition issue.
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