How to Reduce DynamoDB Write Throttling with Provisioned Capacity
A company uses Amazon DynamoDB with provisioned capacity for a critical workload. They notice that write requests are being throttled during peak hours. The table has a partition key of 'user_id' and a sort key of 'timestamp'. The access pattern is evenly distributed. What should a solutions architect do to reduce throttling?
Quick Answer
The answer is to enable DynamoDB Auto Scaling for write capacity. This is the correct solution because Auto Scaling dynamically adjusts the provisioned write capacity units based on actual traffic patterns, automatically increasing throughput during peak hours to prevent write throttling and scaling down during off-peak times to control costs. On the AWS Certified Solutions Architect Professional SAP-C02 exam, this question tests your understanding of how to balance performance and cost with provisioned capacity, often presenting traps like suggesting DAX (which only caches reads) or increasing read capacity (which does nothing for writes). A common memory tip is to remember that Auto Scaling is the "set and forget" solution for predictable workloads, whereas on-demand is the premium option for unpredictable spikes. Think of it as a thermostat: Auto Scaling adjusts the temperature (capacity) to keep the room (your table) comfortable, while on-demand just blasts the AC full power regardless of cost.
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 write capacity.
DynamoDB Auto Scaling adjusts write capacity based on actual usage, reducing throttling without manual intervention. Option A is wrong because increasing read capacity does not help with write throttling. Option B is wrong because DAX is a cache for read operations, not writes. Option C is wrong because while switching to On-Demand mode can eliminate throttling, it may be more expensive than Auto Scaling if the workload is predictable; Auto Scaling is the more cost-effective solution for this scenario.
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 capacity units for the table.
Why it's wrong here
Read capacity does not affect write throttling.
- ✗
Add a DynamoDB Accelerator (DAX) cluster in front of the table.
Why it's wrong here
DAX caches reads, not writes.
- ✗
Switch the table to DynamoDB On-Demand capacity mode.
Why it's wrong here
Switching to On-Demand mode eliminates throttling by removing the need to manage write capacity, but it fails to address the underlying cost-efficiency requirement for a critical workload with predictable peak-hour demand. On-Demand is designed for unpredictable traffic patterns where capacity planning is impractical, not for a steady, evenly distributed access pattern where provisioned capacity with auto-scaling would maintain throughput without incurring On-Demand’s higher per-request cost.
- ✓
Enable DynamoDB Auto Scaling for write capacity.
Why this is correct
Auto Scaling dynamically adjusts write capacity to handle peak loads.
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Same concept, more angles
1 more way this is tested on SAP-C02
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 on-demand capacity. They notice that a specific table frequently exceeds the write throughput limit and experiences throttling. The table has a partition key with high cardinality. What is the MOST likely cause of throttling?
hard- ✓ A.A sudden spike in write traffic exceeds the table's burst capacity
- B.The table is using DynamoDB Accelerator (DAX) for writes
- C.The write workload exceeds the table's provisioned write capacity
- D.The table has auto scaling disabled
Why A: On-demand DynamoDB tables can handle traffic spikes up to double the previous peak. If write traffic exceeds that threshold, throttling occurs. The table's burst capacity is limited to the previous peak for on-demand, so a sudden spike exceeding that will cause throttling. Option B is incorrect because DAX is a caching layer for reads, not writes, and does not affect write throughput. Option C is incorrect because the table is using on-demand capacity, not provisioned; on-demand does not have provisioned write capacity. Option D is incorrect because auto scaling is not applicable for on-demand tables; on-demand automatically scales, but still has a limit based on previous peak.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This SAP-C02 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the SAP-C02 exam.