- A
Reduce the batch size in the event source mapping.
Why wrong: Smaller batches mean more invocations, which can increase overhead.
- B
Increase the Lambda function timeout to 15 minutes.
Why wrong: Timeout alone does not help with high data volume; processing still takes time.
- C
Increase the Lambda function memory and set reserved concurrency.
Why wrong: Memory helps CPU, but reserved concurrency limits parallelism.
- D
Increase the number of shards and use a Kinesis Data Analytics application for windowed aggregation before Lambda.
More shards increase parallelism, and pre-aggregation reduces Lambda load.
Question 1,446 of 1,786
DEA-C01 Data Operations and Support Practice Question
This DEA-C01 practice question tests your understanding of data operations and support. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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.
Option D is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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 |
What to study next
Got this wrong? Here's your next step.
Identify which DEA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Questions learners often ask
What does this DEA-C01 question test?
Data Operations and Support — This question tests Data Operations and Support — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Increase the number of shards and use a Kinesis Data Analytics application for windowed aggregation before Lambda. — Option D is correct because 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.
What should I do if I get this DEA-C01 question wrong?
Identify which DEA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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Last reviewed: Jun 20, 2026
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