DEA-C01 Data Ingestion and Transformation Practice Question
A company is using Amazon Kinesis Data Streams to process real-time stock trade data. The data is consumed by a Lambda function that calculates moving averages and stores results in Amazon DynamoDB. The Lambda function is failing with 'ProvisionedThroughputExceededException' on the DynamoDB table. The table has on-demand capacity. Which TWO actions should the engineer take to resolve this issue?
⚠ Common exam trap
Candidates often think increasing concurrency (Option C) helps with DynamoDB throttling, but it actually increases write pressure, while the correct approach is to reduce the request rate via batching (Option D) and handle retries with exponential backoff (Option A).
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
✓
Add a dead-letter queue and configure the Lambda function to retry on failure with exponential backoff.
Adding a dead-letter queue (DLQ) and configuring the Lambda function to retry on failure with exponential backoff allows the function to handle transient ProvisionedThroughputExceededExceptions from DynamoDB. Since the table uses on-demand capacity, the exception indicates a momentary throttle due to traffic spikes; exponential backoff with retries gives DynamoDB time to scale up, while the DLQ captures records that persistently fail for later analysis.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Add a dead-letter queue and configure the Lambda function to retry on failure with exponential backoff.
Why this is correct
Retries with backoff help handle throttling gracefully.
- ✗
Decrease the batch window to 0 seconds to process records immediately.
Why it's wrong here
This increases write frequency.
- ✗
Increase the Lambda function's reserved concurrency to process more shards.
Why it's wrong here
More concurrency = more writes, exacerbating the issue.
- ✓
Increase the batch size of the Kinesis event source mapping for the Lambda function.
Why this is correct
Larger batches mean fewer writes, reducing the request rate to DynamoDB.
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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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.