DEA-C01 Data Store Management Practice Question
A company runs a real-time analytics platform on AWS. Data is ingested from thousands of IoT devices into Amazon Kinesis Data Streams. A Lambda function consumes the stream, processes the data, and writes the results to an Amazon DynamoDB table. The DynamoDB table has a provisioned write capacity of 1000 WCU, and the read capacity is set to 200 RCU. Recently, the company noticed that the Lambda function is failing with ProvisionedThroughputExceededException on DynamoDB writes. The Lambda function is configured with a batch size of 100 and a concurrency limit of 10. The Kinesis shard count is 4. The number of devices has increased, but the data volume per device has remained the same. The company needs to resolve the write throttling without increasing the DynamoDB write capacity. Which action should the data engineer take?
⚠ Common exam trap
Watch out — candidates often assume increasing concurrency or shards will distribute the load better, but in reality, those actions increase the total write throughput, exacerbating throttling when DynamoDB capacity is fixed.
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
✓
Reduce the batch size of the Lambda function to 10.
Reducing the batch size from 100 to 10 decreases the number of records processed per Lambda invocation, which reduces the burst of write requests to DynamoDB per invocation. This helps stay within the 1000 WCU limit without increasing capacity, as the same total throughput is spread across more invocations with smaller batches.
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 number of Kinesis shards to 8.
Why it's wrong here
More shards increase parallelism, worsening throttling.
- ✗
Increase the Lambda concurrency limit to 20.
Why it's wrong here
More concurrency increases write pressure.
- ✗
Increase the batch size to 200.
Why it's wrong here
Larger batches increase write volume per invocation.
- ✓
Reduce the batch size of the Lambda function to 10.
Why this is correct
Smaller batches reduce write volume per invocation.
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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