DEA-C01 Data Operations and Support Practice Question
A company runs a data pipeline that ingests streaming data via Amazon Kinesis Data Streams, processes it with an AWS Lambda function, and stores results in Amazon DynamoDB. The Lambda function sometimes fails due to 'ProvisionedThroughputExceededException' on the DynamoDB table. Which combination of steps should a data engineer take to resolve this issue?
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 and configure a dead-letter queue for the Lambda function.
Enabling DynamoDB auto scaling allows the table to adjust its provisioned throughput based on actual traffic patterns, which helps prevent 'ProvisionedThroughputExceededException' when the Lambda function writes to DynamoDB. Additionally, configuring a dead-letter queue (DLQ) for the Lambda function ensures that records that fail due to throttling are captured and can be reprocessed later, preventing data loss. Option B is incorrect because increasing the Lambda function timeout does not address DynamoDB throughput limits. Option C is incorrect because increasing the number of Kinesis shards may increase the rate of Lambda invocations, potentially worsening the throttling issue. Option D is incorrect because increasing Lambda reserved concurrency could allow more concurrent invocations, which may exacerbate throughput exceedance, and disabling retries would cause data loss.
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 auto scaling and configure a dead-letter queue for the Lambda function.
Why this is correct
Auto scaling adjusts throughput; DLQ captures failed records for reprocessing.
- ✗
Increase the Lambda function timeout and enable batch windows.
Why it's wrong here
Timeout increase does not reduce DynamoDB request rate.
- ✗
Increase the number of Kinesis shards to reduce Lambda invocations.
Why it's wrong here
More shards increase Lambda invocations, potentially worsening throttling.
- ✗
Increase Lambda reserved concurrency and disable retries.
Why it's wrong here
Disabling retries may cause data loss.
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 |
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 →
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.