DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer is designing a data ingestion pipeline that uses AWS Lambda to process records from a Kinesis Data Stream and write to DynamoDB. Which TWO strategies can help handle increased throughput and prevent data loss? (Choose TWO.)
⚠ Common exam trap
Watch out — candidates often think a dead-letter queue (DLQ) prevents data loss during high throughput, but DLQs only capture records after processing failures, not during ingestion spikes, and they confuse synchronous invocation (Option B) with the actual asynchronous event source mapping used by Kinesis.
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
✓
Configure the Lambda event source mapping with a batch window and set the number of concurrent batches per shard
Configuring a batch window allows Lambda to accumulate records from the Kinesis stream for up to 300 seconds before invoking the function, which helps smooth out traffic spikes and reduces the number of invocations. Setting the number of concurrent batches per shard (via the ParallelizationFactor, up to 10) enables Lambda to process multiple batches from the same shard in parallel, increasing throughput without data loss. This combination ensures that records are processed efficiently even under high load, as Lambda can handle more concurrent executions per shard while batching reduces the risk of throttling.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Configure the Lambda event source mapping with a batch window and set the number of concurrent batches per shard
Why this is correct
This improves throughput and handles spikes.
- ✗
Use synchronous invocation of Lambda from the producer
Why it's wrong here
Synchronous invocation can cause throttling and is not recommended for streaming.
- ✓
Increase the number of shards in the Kinesis data stream
Why this is correct
More shards increase parallelism and throughput.
- ✗
Configure a dead-letter queue (DLQ) for the Lambda function
Why it's wrong here
DLQ captures failures but does not prevent loss; retries are needed.
- ✗
Increase the Lambda function timeout
Why it's wrong here
Timeout does not increase throughput.
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.