The answer is to increase the batch size in the event source mapping. This is correct because when the Kinesis consumer Lambda shows a rising IteratorAgeMilliseconds but adequate individual invocation duration, the bottleneck is throughput per invocation, not execution speed. By processing more records per Lambda call, you increase the overall consumption rate without needing more shards or concurrency. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of Kinesis stream consumption patterns and event source mapping tuning, often appearing as a trap where candidates mistakenly focus on scaling shards or Lambda concurrency instead of batch size. A common memory tip: think of "lag" as a line at a ticket counter—if each customer (invocation) can handle more tickets (records), the line moves faster. Remember: for Kinesis consumer lag, first check batch size and parallelization factor before scaling infrastructure.
MLS-C01 Data Engineering Practice Question
This MLS-C01 practice question tests your understanding of data engineering. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 real-time recommendation system that uses Amazon SageMaker endpoints for inference. The system ingests user activity data from a mobile app via Amazon API Gateway and AWS Lambda, which writes events to an Amazon Kinesis Data Stream. A second Lambda function consumes the stream, calls a SageMaker endpoint to generate recommendations, and stores the results in Amazon DynamoDB. The system has been working well, but recently the team noticed an increase in latency from the time a user action occurs to when the recommendation is stored. The SageMaker endpoint shows increased invocation latency but no throttling. CloudWatch metrics show that the Kinesis stream's IteratorAgeMilliseconds is increasing, indicating the consumer is falling behind. The Lambda consumer's duration is within limits, but the number of invocations is lower than expected. The team suspects the issue is with the event source mapping. Which course of action should the team take to reduce the latency?
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 event source mapping to process more records per invocation.
Option B is correct because the consumer is falling behind despite adequate Lambda duration, suggesting that the batch size or parallelization factor is too low. Increasing the batch size allows each Lambda invocation to process more records, increasing throughput. Option A is wrong because increasing shards increases cost and may not help if the consumer is the bottleneck. Option C is wrong because reducing concurrency would worsen the situation. Option D is wrong because the Lambda function is already consuming from Kinesis; using Firehose would not directly solve the consumer lag.
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 batch size in the event source mapping to process more records per invocation.
Why this is correct
Larger batches improve throughput by reducing overhead per invocation.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the number of shards in the Kinesis data stream to increase parallelism.
Why it's wrong here
The consumer is already falling behind; more shards would require more Lambda concurrency but the event source mapping may be limiting.
✗
Decrease the Lambda function's reserved concurrency to force it to scale down.
Replace the Lambda consumer with an Amazon Kinesis Data Firehose delivery stream.
Why it's wrong here
Firehose is for near-real-time delivery, not for calling SageMaker endpoints.
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 MLS-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 Engineering — This question tests Data Engineering — 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 event source mapping to process more records per invocation. — Option B is correct because the consumer is falling behind despite adequate Lambda duration, suggesting that the batch size or parallelization factor is too low. Increasing the batch size allows each Lambda invocation to process more records, increasing throughput. Option A is wrong because increasing shards increases cost and may not help if the consumer is the bottleneck. Option C is wrong because reducing concurrency would worsen the situation. Option D is wrong because the Lambda function is already consuming from Kinesis; using Firehose would not directly solve the consumer lag.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-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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