The answer is increasing the Batch Window (MaximumBatchingWindowInSeconds) for the event source mapping, as this is the most cost-effective way to handle Lambda throttling from Kinesis. By extending the batch window, Lambda accumulates more records from the Kinesis stream—each record is about 50 KB across 10 shards—before invoking the function, which reduces the number of concurrent executions and directly lowers throttling and error rates without incurring additional costs for more shards, memory, or parallelization. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your understanding of event source mapping tuning parameters and the trade-off between latency and throughput; a common trap is immediately choosing to increase shard count or Lambda concurrency, which are more expensive. Remember the memory tip: “Window wins without waste”—the batch window is the cheapest lever to pull when throttling stems from too many invocations, not from insufficient processing power.
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 company uses AWS Lambda to process records from an Amazon Kinesis Data Stream. Each record is about 50 KB. The Lambda function transforms the data and writes to Amazon DynamoDB. Recently, the Lambda function has been experiencing throttling and high error rates. The Kinesis stream has 10 shards. What is the most cost-effective solution to improve processing throughput?
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 Window (MaximumBatchingWindowInSeconds) for the event source mapping.
Option D is correct because increasing the Batch Window (MaximumBatchingWindowInSeconds) allows the Lambda function to accumulate more records from the Kinesis stream before invoking the function, reducing the number of invocations and thus lowering the chance of throttling. This is the most cost-effective solution as it does not require additional shards, memory, or parallelization, and directly addresses the high error rates caused by excessive concurrent executions.
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 number of shards in the Kinesis stream.
Why it's wrong here
Increases cost and may not be necessary.
✗
Increase the Parallelization Factor for the Lambda event source mapping.
Why it's wrong here
May increase concurrency but not cost-effective.
✗
Increase the memory allocated to the Lambda function.
Why it's wrong here
Does not reduce invocation rate.
✓
Increase the Batch Window (MaximumBatchingWindowInSeconds) for the event source mapping.
Why this is correct
Reduces invocation frequency.
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 often assume increasing shards or parallelization is the only way to improve throughput, but the question specifically asks for the most cost-effective solution, and increasing the batch window reduces invocation count without incurring additional costs.
Detailed technical explanation
How to think about this question
Under the hood, the Kinesis event source mapping uses a batch window to buffer records before invoking Lambda; the default is 0 seconds (immediate invocation), but increasing it to up to 300 seconds allows batching more records per invocation, reducing the number of invocations and thus the risk of hitting Lambda concurrency limits. This is particularly effective when each record is small (50 KB) and the stream has many shards, as it amortizes the overhead of each invocation across more records, improving throughput without additional infrastructure costs.
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 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 exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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: Increase the Batch Window (MaximumBatchingWindowInSeconds) for the event source mapping. — Option D is correct because increasing the Batch Window (MaximumBatchingWindowInSeconds) allows the Lambda function to accumulate more records from the Kinesis stream before invoking the function, reducing the number of invocations and thus lowering the chance of throttling. This is the most cost-effective solution as it does not require additional shards, memory, or parallelization, and directly addresses the high error rates caused by excessive concurrent executions.
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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