The answer is to increase the batch size in the Lambda event source mapping. This is correct because each Lambda invocation processes a larger chunk of records from the Kinesis stream, which directly reduces the total number of invocations and the polling frequency. Since Kinesis enforces a hard read throughput limit of 5 transactions per second per shard, fewer invocations mean you are less likely to exceed that limit and trigger throttling errors. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your understanding of how Lambda’s event source mapping interacts with Kinesis shard limits—a common trap is to assume you need to increase concurrency or add more shards, but for simple, I/O-bound transformations, batch size is the lever. A useful memory tip: think of it as “more records per trip, fewer trips per shard” to avoid the throttle.
DEA-C01 Data Operations and Support Practice Question
This DEA-C01 practice question tests your understanding of data operations and support. 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 company runs a data pipeline using AWS Lambda to process records from an Amazon Kinesis Data Stream. Recently, the Lambda function has been experiencing high invocation errors and the stream is throttling. The function performs simple transformations and writes to Amazon S3. What is the most effective way to reduce throttling and errors?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase the batch size in the Lambda event source mapping.
Increasing the batch size in the Lambda event source mapping allows each invocation to process more records from the Kinesis stream, reducing the number of total invocations. This lowers the rate at which Lambda polls the stream, which decreases the likelihood of hitting the Kinesis read throughput limits (5 transactions per second per shard) and reduces throttling errors. The simple transformations and S3 writes are likely I/O-bound, so larger batches improve throughput without increasing invocation concurrency.
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.
✗
Increase the Lambda function timeout.
Why it's wrong here
Timeout does not affect throttling or invocation errors.
✗
Enable provisioned concurrency on the Lambda function.
Why it's wrong here
Provisioned concurrency keeps instances warm but does not reduce throttling from the stream.
✗
Increase the number of shards in the Kinesis stream.
Why it's wrong here
More shards increase parallelism and could increase throttling if not needed.
✓
Increase the batch size in the Lambda event source mapping.
Why this is correct
Larger batch sizes mean fewer invocations, reducing throttling and errors.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates mistakenly believe throttling is caused by Lambda concurrency limits or cold starts, when in fact the root cause is the Kinesis stream's read throughput limit per shard, which is reduced by increasing the batch size in the event source mapping.
Detailed technical explanation
How to think about this question
Lambda's event source mapping for Kinesis uses a shard-level iterator that polls each shard with a maximum of 5 GetRecords requests per second. By increasing the batch size (up to 10,000 records or 6 MB), each GetRecords call retrieves more data, reducing the number of polling requests and thus the risk of exceeding the shard read throughput limit. In real-world scenarios, if the transformation logic is lightweight (e.g., simple JSON parsing and S3 PutObject), the bottleneck is often the Kinesis read API calls, not the compute time, making batch size tuning the most effective lever.
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.
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
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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 batch size in the Lambda event source mapping. — Increasing the batch size in the Lambda event source mapping allows each invocation to process more records from the Kinesis stream, reducing the number of total invocations. This lowers the rate at which Lambda polls the stream, which decreases the likelihood of hitting the Kinesis read throughput limits (5 transactions per second per shard) and reduces throttling errors. The simple transformations and S3 writes are likely I/O-bound, so larger batches improve throughput without increasing invocation concurrency.
What should I do if I get this DEA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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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