- A
Reduce the number of shards in the Kinesis stream to limit concurrency.
Why wrong: Incorrect. Reducing the number of shards in the Kinesis stream to limit concurrency would decrease throughput and cause backpressure, not reduce invocations effectively.
- B
Increase the Lambda function's memory allocation to improve performance.
Why wrong: Incorrect. Increasing the Lambda function's memory allocation to improve performance only speeds up individual executions but does not change the fact that each record triggers an invocation.
- C
Replace the Lambda function with Amazon Kinesis Data Firehose and use its built-in transformation.
Why wrong: Incorrect. While Kinesis Data Firehose can batch records, it still requires a Lambda function for custom transformation per record, or uses limited built-in transformations. It does not solve the issue of high invocation count for custom logic.
- D
Configure the event source mapping to use a larger batch size and set a batch window.
Correct. Configuring the event source mapping to use a larger batch size and set a batch window allows Lambda to process multiple records in a single invocation, drastically reducing invocation count and improving throughput.
DEA-C01 Event source mapping 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. A key principle to apply: event source mapping. 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 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 event source mapping to use a larger batch size and set a batch window.
Option D is correct. Configuring the event source mapping with a larger batch size and a batch window allows Lambda to process multiple records per invocation, reducing the number of invocations and costs. This improves throughput and reduces throttling. Option A is incorrect because reducing shards reduces the stream capacity, causing backpressure and potential data loss. Option B is incorrect because increasing memory does not reduce the number of invocations; it only speeds up processing per invocation, but still processes one record at a time. Option C is incorrect because Kinesis Data Firehose can batch records, but it still uses per-record Lambda transformation if you use a Lambda function, or it can use built-in transformations but not the flexible logic described. The most direct solution is to batch records in the existing Lambda function via event source mapping parameters.
Key principle: Event source mapping
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
Incorrect. Reducing the number of shards in the Kinesis stream to limit concurrency would decrease throughput and cause backpressure, not reduce invocations effectively.
- ✗
Increase the Lambda function's memory allocation to improve performance.
Why it's wrong here
Incorrect. Increasing the Lambda function's memory allocation to improve performance only speeds up individual executions but does not change the fact that each record triggers an invocation.
- ✗
Replace the Lambda function with Amazon Kinesis Data Firehose and use its built-in transformation.
Why it's wrong here
Incorrect. While Kinesis Data Firehose can batch records, it still requires a Lambda function for custom transformation per record, or uses limited built-in transformations. It does not solve the issue of high invocation count for custom logic.
- ✓
Configure the event source mapping to use a larger batch size and set a batch window.
Why this is correct
Correct. Configuring the event source mapping to use a larger batch size and set a batch window allows Lambda to process multiple records in a single invocation, drastically reducing invocation count and improving throughput.
Related concept
Event source mapping
Common exam traps
Common exam trap: answer the scenario, not the keyword
A candidate might think that reducing the number of shards will reduce invocations, but that actually reduces the stream's ability to handle the data volume and can cause throttling or data loss.
Detailed technical explanation
How to think about this question
Treat this as a scenario question. Identify the problem, the constraint, and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- Event source mapping
- Batch size
- Batch window
- Lambda invocation
TExam Day Tips
- 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
Event source mapping
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
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
What to study next
Got this wrong? Here's your next step.
Review event source mapping, then practise related DEA-C01 questions on the same topic to reinforce the concept.
- →
Data Ingestion and Transformation — study guide chapter
Learn the concepts, then practise the questions
- →
Data Ingestion and Transformation practice questions
Targeted practice on this topic area only
- →
All DEA-C01 questions
1,786 questions across all exam domains
- →
AWS Certified Data Engineer Associate DEA-C01 study guide
Full concept coverage aligned to exam objectives
- →
DEA-C01 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related DEA-C01 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Data Ingestion and Transformation practice questions
Practise DEA-C01 questions linked to Data Ingestion and Transformation.
Data Operations and Support practice questions
Practise DEA-C01 questions linked to Data Operations and Support.
Data Security and Governance practice questions
Practise DEA-C01 questions linked to Data Security and Governance.
Data Store Management practice questions
Practise DEA-C01 questions linked to Data Store Management.
DEA-C01 fundamentals practice questions
Practise DEA-C01 questions linked to DEA-C01 fundamentals.
DEA-C01 scenario practice questions
Practise DEA-C01 questions linked to DEA-C01 scenario.
DEA-C01 troubleshooting practice questions
Practise DEA-C01 questions linked to DEA-C01 troubleshooting.
Practice this exam
Start a free DEA-C01 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Questions learners often ask
What does this DEA-C01 question test?
Data Ingestion and Transformation — This question tests Data Ingestion and Transformation — Event source mapping.
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 D is correct. Configuring the event source mapping with a larger batch size and a batch window allows Lambda to process multiple records per invocation, reducing the number of invocations and costs. This improves throughput and reduces throttling. Option A is incorrect because reducing shards reduces the stream capacity, causing backpressure and potential data loss. Option B is incorrect because increasing memory does not reduce the number of invocations; it only speeds up processing per invocation, but still processes one record at a time. Option C is incorrect because Kinesis Data Firehose can batch records, but it still uses per-record Lambda transformation if you use a Lambda function, or it can use built-in transformations but not the flexible logic described. The most direct solution is to batch records in the existing Lambda function via event source mapping parameters.
What should I do if I get this DEA-C01 question wrong?
Review event source mapping, then practise related DEA-C01 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Event source mapping
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 →
Keep practising
More DEA-C01 practice questions
- A data pipeline uses Kinesis Data Firehose to deliver streaming data to an S3 bucket. The data volume spikes occasionall…
- An e-commerce company uses AWS Glue to run ETL jobs that transform clickstream data from Amazon S3. The job reads Parque…
- A data engineering team uses Amazon Kinesis Data Analytics for Apache Flink to process streaming data. They notice that…
- A company uses AWS Glue to process streaming data from Amazon Kinesis Data Streams. The job reads JSON records and write…
- A data engineer applies the above bucket policy to an S3 bucket containing sensitive data. The goal is to allow only enc…
- A company uses AWS Glue to catalog data in Amazon S3. The security team requires that all sensitive data be identified a…
Last reviewed: Jun 20, 2026
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.