How to Resolve DynamoDB Auto Scaling Throttling
A company is using Amazon DynamoDB with auto scaling enabled. The table has a provisioned read capacity of 10,000 RCU and write capacity of 5,000 WCU. Auto scaling target utilization is 70%. The table experiences a sudden spike in read traffic, reaching 12,000 RCU. The table throttles some requests. Which THREE actions should the company take to prevent future throttling?
Quick Answer
The answer is to enable DAX to cache read requests, increase the maximum read capacity, and implement exponential backoff. This combination directly addresses the root cause of DynamoDB auto scaling throttling: auto scaling reacts to sustained load, not sudden spikes, so a burst of 12,000 RCU against a 10,000 RCU ceiling will always cause throttling until scaling completes. DAX offloads read traffic from the table, reducing the effective RCU demand, while raising the maximum capacity ensures the table can handle peak loads without waiting for auto scaling to catch up. Exponential backoff, meanwhile, is a best practice for client-side retry logic that prevents overwhelming the table during transient throttling events. On the AWS Certified Database Specialty DBS-C01 exam, this scenario tests your understanding that auto scaling is reactive, not proactive—a common trap is assuming lowering target utilization (e.g., to 50%) would help, but that only triggers scaling earlier on sustained traffic, not during instantaneous bursts. Memory tip: think “DAX for reads, raise the max, and back off” to remember the three pillars of throttling resolution.
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 exponential backoff in the application to retry throttled requests.
Implementing exponential backoff with jitter helps handle throttled requests gracefully by retrying after increasing delays, reducing further load on the table. Option B is correct because increasing the maximum read capacity in the auto scaling configuration allows the table to scale up further (beyond 10,000 RCU) during traffic spikes, preventing throttling. Option E is correct because enabling DynamoDB Accelerator (DAX) caches read requests, offloading the table and reducing the read load that can cause throttling. Option C is wrong because decreasing the target utilization would cause auto scaling to trigger earlier, but it does not increase the maximum capacity; the table would still be limited to the max capacity set, and the spike might exceed it. Option D is wrong because the issue is read traffic, not write capacity, so increasing write capacity does not address read throttling.
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 exponential backoff in the application to retry throttled requests.
Why this is correct
Exponential backoff helps handle throttled requests gracefully by retrying with delays.
- ✓
Increase the maximum read capacity in the auto scaling configuration.
Why this is correct
Increasing the maximum read capacity in auto scaling allows the table to scale higher to accommodate spikes.
- ✗
Decrease the auto scaling target utilization to 50% to scale out earlier.
Why it's wrong here
Decreasing target utilization would trigger scaling at lower usage, but since auto scaling is reactive, it may not prevent throttling during sudden spikes.
- ✗
Increase the write capacity to 10,000 WCU.
Why it's wrong here
Increasing write capacity does not address the read capacity issue.
- ✓
Enable DAX to cache read requests and reduce the load on the table.
Why this is correct
DAX caches reads, reducing the load on the base table.
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Same concept, more angles
4 more ways this is tested on DBS-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company uses Amazon DynamoDB with auto scaling enabled. They notice that a table's write capacity is frequently throttled during a specific hour each day. The access pattern is uniform across partitions. Which action would resolve the throttling without manual intervention?
hard- A.Enable DynamoDB Accelerator (DAX) to cache writes.
- B.Disable auto scaling and set a fixed higher capacity.
- C.Create a larger number of partitions by splitting the table.
- ✓ D.Increase the minimum provisioned capacity in auto scaling.
Why D: When DynamoDB auto scaling is enabled, throttling can occur if the table's write capacity demand spikes faster than auto scaling can increase capacity. The 'minimum provisioned capacity' in the auto scaling policy sets a floor for the capacity units. By increasing this minimum, the table starts with a higher base capacity, reducing the likelihood of throttling during predictable peak hours. Option A is incorrect because DAX is a caching layer for reads, not writes. Option B is incorrect because disabling auto scaling requires manual intervention. Option C is incorrect because table partitioning in DynamoDB is managed automatically based on provisioned capacity; you cannot manually split partitions.
Variation 2. A company is using Amazon DynamoDB with auto scaling enabled. The application is experiencing higher than expected write throttling. Which action should be taken to resolve this issue?
medium- ✓ A.Increase the minimum provisioned capacity for the table.
- B.Disable auto scaling and set a fixed provisioned capacity.
- C.Decrease the maximum provisioned capacity to limit writes.
- D.Switch the table to on-demand capacity mode.
Why A: Auto scaling adjusts capacity based on workload, but it can lag behind sudden traffic spikes, causing write throttling. Increasing the minimum provisioned capacity ensures a baseline capacity that can accommodate predictable bursts, reducing throttling. Option A (increase min capacity) is correct. Option B (disable auto scaling and set fixed capacity) would remove the benefit of dynamic scaling and may not handle varying loads. Option C (decrease max capacity) would limit the table's ability to scale up, potentially worsening throttling. Option D (switch to on-demand) could eliminate throttling but at higher cost, and the question asks for a resolution while keeping auto scaling enabled.
Variation 3. A company is using Amazon DynamoDB with auto scaling enabled. Despite auto scaling, the application is still experiencing throttling during traffic spikes. Which THREE actions should the company take to resolve this issue? (Choose THREE.)
hard- ✓ A.Implement exponential backoff in the application code
- ✓ B.Enable DynamoDB Accelerator (DAX) to cache read-heavy workloads
- ✓ C.Use DynamoDB global tables to distribute write traffic across regions
- D.Switch to on-demand capacity mode
- E.Disable auto scaling and set fixed capacity
Why A: Exponential backoff (A) is a best practice to retry throttled requests gracefully, reducing retry storms. DAX (B) caches read-heavy workloads, reducing read capacity unit consumption and mitigating hot key issues. Global tables (C) distribute write traffic across multiple regions, alleviating write throttling. Option D (on-demand capacity) could help but may be cost-prohibitive and does not address hot keys. Option E (disable auto scaling) would worsen throttling by fixing capacity. Therefore, A, B, and C are correct.
Variation 4. A team is using Amazon DynamoDB with auto scaling enabled. They notice that some requests are returning ProvisionedThroughputExceededException errors during a sudden traffic spike. The application uses strong consistent reads. Which two actions would help mitigate the throttling without over-provisioning capacity? (Choose two.)
hard- ✓ A.Implement DynamoDB Accelerator (DAX) to cache read results.
- ✓ B.Enable DynamoDB adaptive capacity.
- C.Switch to eventually consistent reads for all queries.
- D.Disable auto scaling and manually set higher capacity.
- E.Use DynamoDB burst capacity for the spike.
Why A: DAX acts as an in-memory cache for DynamoDB, reducing the number of read requests that consume provisioned throughput, thus mitigating throttling without increasing capacity. Option B is correct because adaptive capacity enables DynamoDB to automatically use unused throughput from other partitions to absorb traffic spikes, reducing ProvisionedThroughputExceededException errors. Option C is wrong because while eventually consistent reads consume half the read capacity, the application uses strong consistent reads, which may be required for data consistency, and this switch may not be acceptable. Option D is wrong because disabling auto scaling and manually setting higher capacity would lead to over-provisioning and increased cost, contrary to the goal of mitigating throttling without over-provisioning. Option E is wrong because burst capacity is limited and not guaranteed; it can handle short spikes but is not a reliable mitigation for sudden spikes.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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