- A
Increase the number of shards in the Kinesis Data Stream to 20 to increase the parallelism of Lambda consumers.
More shards allow more concurrent Lambda invocations, improving throughput and reducing iterator age.
- B
Increase the Lambda reserved concurrency to 5000 to allow more parallel executions.
Why wrong: Higher concurrency may not help if the shard count is the bottleneck; each shard limits the number of concurrent Lambda invocations.
- C
Increase the batch window to 300 seconds to accumulate more records per invocation and reduce the number of calls.
Why wrong: Longer batch window increases latency and may not reduce backlog if the data arrival rate is high.
- D
Switch to using Amazon Kinesis Data Analytics with a larger instance type to process the stream.
Why wrong: Kinesis Data Analytics is a different service and would require rearchitecting; it is more complex and costly.
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 retail company runs an e-commerce platform on AWS. They have a Data Engineering team that processes clickstream data using Amazon Kinesis Data Streams (KDS) with a shard count of 5. The data is consumed by an AWS Lambda function that transforms and loads the data into an Amazon S3 bucket partitioned by year/month/day/hour. Recently, the team has noticed that the Lambda function is experiencing throttling errors, and the KDS shard iterator age is increasing, indicating that the consumer cannot keep up with the incoming data rate. The team has already increased the Lambda reserved concurrency to 1000 and enabled batch window of 60 seconds. The metrics show that the Lambda function duration is well under the 5-minute timeout, and there are no errors in the transformation logic. The S3 write operations are not failing. Which course of action would MOST effectively resolve the issue without unnecessary cost or complexity?
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
Increase the number of shards in the Kinesis Data Stream to 20 to increase the parallelism of Lambda consumers.
The core issue is that the Lambda consumer cannot keep up with the incoming data rate, as evidenced by the increasing shard iterator age. Increasing the shard count from 5 to 20 directly increases the number of Kinesis Data Streams shards, which in turn increases the number of concurrent Lambda invocations (one per shard) and the overall throughput of the stream. This addresses the bottleneck at the source without adding unnecessary complexity or cost, as KDS pricing is based on shard hours and Lambda concurrency is already set to 1000.
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 Data Stream to 20 to increase the parallelism of Lambda consumers.
Why this is correct
More shards allow more concurrent Lambda invocations, improving throughput and reducing iterator age.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the Lambda reserved concurrency to 5000 to allow more parallel executions.
Why it's wrong here
Higher concurrency may not help if the shard count is the bottleneck; each shard limits the number of concurrent Lambda invocations.
- ✗
Increase the batch window to 300 seconds to accumulate more records per invocation and reduce the number of calls.
Why it's wrong here
Longer batch window increases latency and may not reduce backlog if the data arrival rate is high.
- ✗
Switch to using Amazon Kinesis Data Analytics with a larger instance type to process the stream.
Why it's wrong here
Kinesis Data Analytics is a different service and would require rearchitecting; it is more complex and costly.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume increasing Lambda concurrency or batch window will solve throughput issues, but they fail to recognize that Kinesis shard count is the fundamental limiter of parallelism in the Lambda-Kinesis integration.
Detailed technical explanation
How to think about this question
Kinesis Data Streams uses shards as the unit of throughput, with each shard supporting up to 1 MB/s write and 2 MB/s read. Lambda integrates with KDS via event source mappings, which poll each shard independently and invoke one Lambda function per shard. The shard iterator age metric indicates how far behind the consumer is from the tip of the stream; if it increases, the consumer is not reading fast enough. Increasing shards increases the number of parallel Lambda invocations, directly improving read throughput.
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.
Visual reference
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.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
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 number of shards in the Kinesis Data Stream to 20 to increase the parallelism of Lambda consumers. — The core issue is that the Lambda consumer cannot keep up with the incoming data rate, as evidenced by the increasing shard iterator age. Increasing the shard count from 5 to 20 directly increases the number of Kinesis Data Streams shards, which in turn increases the number of concurrent Lambda invocations (one per shard) and the overall throughput of the stream. This addresses the bottleneck at the source without adding unnecessary complexity or cost, as KDS pricing is based on shard hours and Lambda concurrency is already set to 1000.
What should I do if I get this MLS-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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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 →
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Last reviewed: Jun 11, 2026
This MLS-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 MLS-C01 exam.
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