DEA-C01 Data Operations and Support Practice Question
A data engineer is designing a data pipeline that ingests streaming data from an IoT fleet using Kinesis Data Streams and processes it with a Lambda function. The Lambda function often times out when the data volume spikes. What is the most scalable solution?
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
✓
Increase the number of shards and use a Kinesis Data Analytics application for windowed aggregation before Lambda.
Increasing the number of shards increases the parallelism of the stream, allowing higher throughput. Using Kinesis Data Analytics for windowed aggregation reduces the volume of data sent to Lambda, preventing timeouts during spikes. Option A is wrong because reducing batch size decreases the number of records per invocation, which can increase the number of Lambda invocations and overhead, but does not address the root cause of timeouts due to volume. Option B is wrong because increasing the Lambda timeout to 15 minutes allows the function to run longer, but it does not increase throughput or handle spikes efficiently; it only delays failures and is not scalable. Option C is wrong because increasing memory can improve performance, but setting reserved concurrency limits the maximum number of concurrent executions, which can throttle processing during spikes and reduce scalability.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reduce the batch size in the event source mapping.
Why it's wrong here
Smaller batches mean more invocations, which can increase overhead.
- ✗
Increase the Lambda function timeout to 15 minutes.
Why it's wrong here
Timeout alone does not help with high data volume; processing still takes time.
- ✗
Increase the Lambda function memory and set reserved concurrency.
Why it's wrong here
Memory helps CPU, but reserved concurrency limits parallelism.
- ✓
Increase the number of shards and use a Kinesis Data Analytics application for windowed aggregation before Lambda.
Why this is correct
More shards increase parallelism, and pre-aggregation reduces Lambda load.
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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