DynamoDB Hot Partition Throttling
A company runs a real-time analytics platform using Amazon Kinesis Data Streams with a shard count of 10. The data is consumed by an AWS Lambda function that writes to an Amazon DynamoDB table. The DynamoDB table has a partition key of 'user_id' and a sort key of 'timestamp'. The table is provisioned with 5000 RCUs and 5000 WCUs. Recently, the application experienced increased write latency and throttling errors (ProvisionedThroughputExceededException) on the DynamoDB table. The CloudWatch metrics show that ConsumedWriteCapacityUnits averages 4500 with occasional spikes to 6000. The Lambda function’s concurrency is set to 1000. The data engineer suspects the issue is due to hot partitions. Upon investigation, the engineer finds that a small number of users generate a disproportionately large amount of data. Which course of action would best resolve the throttling while minimizing cost?
Quick Answer
The answer is to enable DynamoDB adaptive capacity and implement write sharding by adding a suffix to the partition key for high-volume users. This resolves hot partition write throttling because a small number of users are overwhelming a single partition with writes, and write sharding distributes those writes across multiple physical partitions by appending a random or calculated suffix to the user_id, while adaptive capacity automatically adjusts throughput to handle uneven access patterns without increasing provisioned capacity. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your understanding of DynamoDB partition design and the difference between scaling horizontally versus vertically—a common trap is to simply increase WCUs, which wastes cost without fixing the hot partition. Remember the memory tip: “Shard the hot keys, not the wallet,” meaning you spread the load on the partition key rather than spending more on throughput.
⚠ Common exam trap
AWS often tests the misconception that throttling is always solved by increasing total provisioned capacity or switching to on-demand, when the real issue is partition-level hot spots that require key design changes like write sharding.
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 adaptive capacity and implement write sharding by adding a suffix to the partition key for high-volume users
The root cause is hot partitions caused by a small number of high-volume users. Enabling DynamoDB adaptive capacity allows the table to automatically adjust throughput to accommodate uneven access patterns, but the key fix is write sharding — adding a random or calculated suffix to the partition key for those high-volume users. This distributes writes across multiple physical partitions, eliminating the hot spot without requiring a global increase in provisioned capacity, thus resolving throttling while minimizing cost.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable DynamoDB adaptive capacity and implement write sharding by adding a suffix to the partition key for high-volume users
Why this is correct
Adaptive capacity automatically manages partition throughput, and write sharding distributes writes across multiple partitions, reducing hot spots.
- ✗
Increase the provisioned WCUs to 10000 to handle spikes
Why it's wrong here
This increases cost and does not address the root cause of hot partitions; throttling may still occur on hot partitions.
- ✗
Switch the DynamoDB table to on-demand capacity mode
Why it's wrong here
On-demand handles spikes but is more expensive for predictable workloads; it may also still throttle on hot partitions.
- ✗
Reduce the Lambda function concurrency to 100 to limit write requests
Why it's wrong here
This would cause a backlog in Kinesis and increase end-to-end latency.
Visual reference
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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Same concept, more angles
1 more way 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 runs a real-time analytics platform on Amazon ECS that ingests streaming data from Amazon Kinesis Data Streams, processes it, and stores results in Amazon DynamoDB. The data volume spikes unpredictably, causing DynamoDB to throttle write requests. The application uses on-demand capacity mode. The data engineer notices that the throttling occurs on a specific partition due to a hot key. The hot key is a customer ID that receives a disproportionate number of writes. The application cannot change the partition key design immediately. The engineer needs to reduce throttling while maintaining low latency. Which solution is most effective?
hard- A.Switch to provisioned capacity with auto scaling and increase the write capacity units.
- ✓ B.Implement a write buffer using Amazon SQS, and have consumers write to DynamoDB at a controlled rate.
- C.Enable DynamoDB Accelerator (DAX) to cache the hot key writes.
- D.Use DynamoDB Streams to trigger a Lambda function that retries throttled writes.
Why B: Buffering writes through Amazon SQS decouples the ingestion rate from DynamoDB's capacity, allowing consumers to write at a controlled pace. This directly mitigates throttling on the hot key without requiring a partition key redesign, and SQS provides low-latency, durable buffering suitable for real-time analytics.
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.