How to Handle DynamoDB ProvisionedThroughputExceededException with Auto Scaling
A company is using Amazon DynamoDB for a high-traffic application. Users report occasional 'ProvisionedThroughputExceededException' errors. The application uses consistent reads and retries with exponential backoff. What is the MOST efficient way to handle these errors and reduce the number of retries?
Quick Answer
The answer is to enable DynamoDB Auto Scaling for the table, as this is the most efficient way to handle ProvisionedThroughputExceededException errors and reduce retries. DynamoDB Auto Scaling works by continuously monitoring the actual consumed throughput against the provisioned capacity, and it automatically adjusts the read and write capacity units in response to real-time traffic patterns, which directly prevents throttling before it becomes a problem. On the AWS Certified Database Specialty DBS-C01 exam, this question tests your understanding of operational efficiency versus manual intervention; a common trap is choosing to switch to eventually consistent reads, which reduces throttling but changes the data consistency model—a trade-off that may violate application requirements. Another trap is manually increasing read capacity, which is reactive and inefficient compared to the proactive, automated scaling that Auto Scaling provides. Memory tip: think “Auto Scale, Auto Heal”—if you let DynamoDB manage its own capacity, you eliminate the need for manual retries and throttling fixes.
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 adjusts the provisioned throughput automatically in response to traffic patterns, reducing throttling and the need for retries. Option A is wrong because increasing read capacity manually is inefficient and does not adapt to fluctuating traffic. Option B is wrong because switching to eventually consistent reads changes the consistency model, which may not be acceptable for the application's requirements. Option D is wrong because 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.
- ✗
Increase the provisioned read capacity units manually
Why it's wrong here
Increasing read capacity manually is inefficient and does not adapt to fluctuating traffic patterns.
- ✗
Switch to eventually consistent reads
Why it's wrong here
Switching to eventually consistent reads changes the consistency model, which may not be acceptable for the application's requirements.
- ✓
Enable DynamoDB Auto Scaling for the table
Why this is correct
DynamoDB Auto Scaling adjusts the provisioned throughput automatically in response to traffic patterns, reducing throttling and the need for retries.
- ✗
Increase the provisioned write capacity units manually
Why it's wrong here
Increasing write capacity does not address read throttling.
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Same concept, more angles
3 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 developer reports that an application using Amazon DynamoDB is experiencing high latency during peak hours. The table has a provisioned capacity of 500 read capacity units (RCUs) and 500 write capacity units (WCUs). The application uses eventually consistent reads and the table is about 50 GB. The developer notices throttled write requests in CloudWatch. Which action would most effectively reduce write throttling?
medium- A.Enable DynamoDB Accelerator (DAX) for the table.
- B.Create a global secondary index on the table.
- ✓ C.Increase the provisioned write capacity for the table.
- D.Switch from eventually consistent reads to strongly consistent reads.
Why C: The developer reports throttled write requests, which directly indicates that the provisioned write capacity (500 WCUs) is insufficient to handle the peak write traffic. Increasing the provisioned write capacity for the table is the most direct and effective action to eliminate write throttling, as it raises the limit on write operations per second. Option C is correct because it addresses the root cause—write capacity exhaustion—without introducing unnecessary components or changing read behavior.
Variation 2. A developer is troubleshooting an issue where an application using Amazon DynamoDB is receiving occasional 'ThrottlingException' errors. The application uses eventually consistent reads. What is the MOST likely cause of this error?
easy- A.The application is using an incorrect table name
- ✓ B.The read capacity units are set too low for the current traffic pattern
- C.The application is using eventually consistent reads instead of strongly consistent reads
- D.There is a network connectivity issue between the application and DynamoDB
Why B: ThrottlingException occurs when the request rate exceeds the provisioned throughput capacity. Option A is incorrect because an incorrect table name would result in a ResourceNotFoundException, not a ThrottlingException. Option C is incorrect because eventually consistent reads consume half the read capacity units compared to strongly consistent reads, making them less likely to cause throttling. Option D is incorrect because network connectivity issues would typically cause timeout or connection errors, not ThrottlingException.
Variation 3. A company is using Amazon DynamoDB for a gaming leaderboard application. Recently, users have experienced increased latency when updating scores. The DynamoDB table has on-demand capacity mode. The application performs UpdateItem calls with a condition expression. Which action is most likely to reduce the latency?
easy- A.Add a global secondary index (GSI) with the score as the sort key to improve update performance.
- B.Switch the table to provisioned capacity and increase the read capacity units to handle peak load.
- C.Disable conditional writes to reduce the overhead of condition expression evaluation.
- ✓ D.Ensure that there are no throttled requests in the CloudWatch metrics and verify that the table is not experiencing hot partitions.
Why D: Increased latency in DynamoDB can be caused by hot partitions where many requests hit the same partition, leading to throttling even with on-demand capacity if the partition limits are exceeded. Checking CloudWatch metrics for throttled requests and partition metrics helps identify hot partitions. Option A is incorrect because adding a GSI does not improve UpdateItem performance; it only helps query performance. Option B is incorrect: on-demand mode already handles capacity, and increasing read capacity units does not affect write operations like UpdateItem. Option C is incorrect: disabling conditional writes would break the application's concurrency control and does not guarantee latency reduction; condition evaluation is a fast, internal operation.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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