DynamoDB Throttling Handling: Auto Scaling, DAX, and Exponential Backoff
A company is using Amazon DynamoDB for an e-commerce application. The application experiences sudden spikes in traffic, causing throttling errors. The data engineer needs to handle the spikes cost-effectively. Which solution should be used?
Quick Answer
The answer is DynamoDB auto scaling with a target utilization of 70%. This solution is correct because auto scaling dynamically adjusts the provisioned read and write capacity based on actual traffic patterns, scaling up during sudden spikes to prevent throttling errors and scaling down during low traffic to avoid over-provisioning, making it a cost-effective approach to DynamoDB throttling handling. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your understanding of balancing performance and cost under unpredictable workloads; a common trap is choosing DAX (which caches reads but does not handle write throttling) or exponential backoff (which retries requests but does not prevent throttling). Remember the 70% target utilization sweet spot: it leaves headroom for spikes while minimizing waste, and think of it as the “Goldilocks” setting—not too high, not too low.
⚠ Common exam trap
Candidates often confuse caching (DAX) with scaling, or assume on-demand mode is always the best for spikes without considering cost, when the question explicitly requires a cost-effective solution for sudden but intermittent 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
✓
Use DynamoDB auto scaling with a target utilization of 70%.
DynamoDB auto scaling with a target utilization of 70% allows the table to dynamically adjust provisioned read/write capacity based on actual traffic patterns, handling sudden spikes without manual intervention while avoiding over-provisioning. This balances performance and cost by scaling up during spikes and scaling down during low traffic, preventing throttling errors cost-effectively.
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 DynamoDB Accelerator (DAX) to cache reads.
Why it's wrong here
DAX reduces read latency but does not handle write spikes.
- ✗
Switch to DynamoDB on-demand capacity mode.
Why it's wrong here
On-demand can handle spikes but is more expensive for steady traffic.
- ✓
Use DynamoDB auto scaling with a target utilization of 70%.
Why this is correct
Auto scaling adjusts capacity dynamically based on traffic.
- ✗
Provision high read and write capacity units to handle peak traffic.
Why it's wrong here
Overprovisioning wastes cost; spikes may still exceed provisioned capacity.
Go deeper
Related to this question
About these practice questions
Courseiva writes every DEA-C01 question from scratch — 1,711 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
Same concept, more angles
2 more ways this is tested on DEA-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 for a gaming application. The application experiences throttling during peak hours. The table's read and write capacity is provisioned. Which TWO actions can reduce throttling?
medium- A.Enable TTL (time to live) on the table to automatically delete old items
- ✓ B.Enable DynamoDB auto scaling for the table
- C.Increase the provisioned read capacity units (RCUs)
- ✓ D.Implement DynamoDB Accelerator (DAX) to cache read requests
- E.Add a DynamoDB Global Table for the table
Why B: DynamoDB auto scaling (Option B) automatically adjusts the provisioned read and write capacity based on actual traffic patterns, preventing throttling during peak hours without manual intervention. This is the correct action because it dynamically increases capacity when demand spikes and reduces it during low traffic, directly addressing the throttling issue.
Variation 2. A company uses Amazon DynamoDB as the primary data store for a web application. The application experiences occasional throttling on write requests. The data engineer needs to implement a solution that handles throttling gracefully without losing data. Which approach should the engineer use?
easy- A.Increase the provisioned write capacity to a higher value
- B.Use an Amazon SQS queue to buffer write requests before sending to DynamoDB
- ✓ C.Implement exponential backoff in the application's write retry logic
- D.Enable DynamoDB Accelerator (DAX) to cache writes
Why C: Implementing exponential backoff in the application's write retry logic is the standard AWS-recommended approach for handling DynamoDB throttling (ProvisionedThroughputExceededException). Exponential backoff gradually increases the wait time between retries, reducing the retry rate and allowing the throttling condition to subside, while ensuring no write data is lost as long as the retries eventually succeed. This approach is lightweight, requires no additional AWS services, and aligns with best practices for building resilient applications against DynamoDB throttling.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DEA-C01 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 DEA-C01 exam.