The answer is to configure the event source mapping to use a larger batch size and set a batch window. This approach directly addresses the need for optimizing Lambda consumer for Kinesis to reduce invocations, because by default Lambda processes one record per invocation, but increasing the batch size allows it to pull multiple records—up to 10,000—from the stream in a single call, while the batch window adds a brief waiting period to accumulate records before triggering the function. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your understanding of Lambda event source mapping tuning for Kinesis, often appearing as a cost-optimization or throughput question where candidates mistakenly choose to increase memory or reduce concurrency. A common trap is assuming Firehose is needed for batching, but Lambda’s native batch settings are simpler and more direct for this use case. Remember the memory tip: “Batch big, wait a bit” to recall that larger batch sizes and a batch window cut invocations without sacrificing throughput.
DEA-C01 Data Ingestion and Transformation Practice Question
This DEA-C01 practice question tests your understanding of data ingestion and transformation. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 building a real-time data pipeline to ingest sensor data from IoT devices. The data is sent to AWS IoT Core, which publishes messages to a Kinesis Data Stream. Each message is about 1 KB in size. The data must be transformed (add a device location field) and then stored in Amazon S3 for long-term analytics. The engineer has set up a Lambda function to transform the records and write to S3. However, the engineer notices that the Lambda function is invoked thousands of times per second, causing high costs and occasional throttling. The Lambda function processes only one record at a time. The engineer wants to reduce the number of Lambda invocations and improve throughput. What should the engineer do?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Configure the event source mapping to use a larger batch size and set a batch window.
Option C is correct because increasing the batch size in the event source mapping allows Lambda to process multiple records per invocation, reducing invocations. Option A is wrong because increasing Lambda memory does not reduce invocations. Option B is wrong because Firehose can batch records, but the transformation would still be per record unless using Firehose's built-in Lambda integration. Option D is wrong because reducing concurrent executions would cause throttling and backlogs.
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 number of shards in the Kinesis stream to limit concurrency.
Why it's wrong here
Reducing shards would reduce throughput and cause data loss.
✗
Increase the Lambda function's memory allocation to improve performance.
Why it's wrong here
More memory may speed up processing but does not reduce invocation count.
✗
Replace the Lambda function with Amazon Kinesis Data Firehose and use its built-in transformation.
Why it's wrong here
Firehose still invokes Lambda per record unless using buffering, but this option suggests replacing, not improving existing setup.
✓
Configure the event source mapping to use a larger batch size and set a batch window.
Why this is correct
Larger batch size and batch window reduce number of invocations.
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.
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.
Data Ingestion and Transformation — This question tests Data Ingestion and Transformation — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Configure the event source mapping to use a larger batch size and set a batch window. — Option C is correct because increasing the batch size in the event source mapping allows Lambda to process multiple records per invocation, reducing invocations. Option A is wrong because increasing Lambda memory does not reduce invocations. Option B is wrong because Firehose can batch records, but the transformation would still be per record unless using Firehose's built-in Lambda integration. Option D is wrong because reducing concurrent executions would cause throttling and backlogs.
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.
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Question Discussion
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