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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 is building a data pipeline to process streaming data from IoT devices. The data is ingested via Amazon Kinesis Data Streams. Each record is about 1 KB. The company wants to use AWS Lambda for real-time transformations and then store the results in Amazon DynamoDB. The expected throughput is 10,000 records per second. The Lambda function currently runs in about 200 ms. The company is concerned about Lambda concurrency limits and wants to ensure there are no throttling errors. The default concurrency limit for Lambda is 1,000. Which approach should the team take to handle the expected throughput without throttling?

A
Increase the Lambda function memory to 3,000 MB to reduce the execution time below 100 ms.
Why wrong: Memory increase may reduce time but not enough to avoid throttling at high invocation rates.
B
Use Amazon Kinesis Data Firehose instead of Lambda to load data directly into DynamoDB.
Why wrong: Kinesis Data Firehose does not support DynamoDB as a destination.
C
Reduce the Lambda batch size to 10 so that each invocation processes fewer records, reducing the time per invocation.
Why wrong: This increases invocations per second to 1,000, which would exceed the concurrency limit of 1,000 if each takes 200 ms.
D
Increase the number of shards in the Kinesis Data Stream to 10 and set the Lambda batch size to 100.
With 10 shards and batch size 100, at most 10 concurrent Lambda invocations, well within limits.

Full breakdown with real-world context →

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 10 and set the Lambda batch size to 100.

Option D is correct because increasing the number of shards to 10 ensures that the Kinesis stream can support up to 10 concurrent Lambda invocations (one per shard). With a batch size of 100, each invocation processes 100 records, resulting in 100 invocations per second (10,000 records / 100 per batch). At 200 ms per invocation, the required concurrency is 100 * 0.2 = 20, well within the default 1,000 concurrency limit. Option A is incorrect because reducing the batch size to 10 would increase invocations to 1,000 per second, requiring 200 concurrent executions, which still fits but is less efficient; the main issue is that reducing batch size does not reduce throttling risk as it increases invocation rate. Option B is incorrect because Kinesis Data Firehose does not natively support Lambda for per-record transformations before writing to DynamoDB; it primarily targets S3, Redshift, or Elasticsearch. Option C is incorrect because increasing Lambda memory typically reduces execution time but does not lower concurrency requirements; moreover, 3,000 MB may not reduce time below 100 ms enough to avoid throttling at high throughput.

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 memory to 3,000 MB to reduce the execution time below 100 ms.
Why it's wrong here
Memory increase may reduce time but not enough to avoid throttling at high invocation rates.
✗
Use Amazon Kinesis Data Firehose instead of Lambda to load data directly into DynamoDB.
Why it's wrong here
Kinesis Data Firehose does not support DynamoDB as a destination.
✗
Reduce the Lambda batch size to 10 so that each invocation processes fewer records, reducing the time per invocation.
Why it's wrong here
This increases invocations per second to 1,000, which would exceed the concurrency limit of 1,000 if each takes 200 ms.
✓
Increase the number of shards in the Kinesis Data Stream to 10 and set the Lambda batch size to 100.
Why this is correct
With 10 shards and batch size 100, at most 10 concurrent Lambda invocations, well within limits.
Related concept
Read the scenario before looking for a memorised answer.

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 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.

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.

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.

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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 10 and set the Lambda batch size to 100. — Option D is correct because increasing the number of shards to 10 ensures that the Kinesis stream can support up to 10 concurrent Lambda invocations (one per shard). With a batch size of 100, each invocation processes 100 records, resulting in 100 invocations per second (10,000 records / 100 per batch). At 200 ms per invocation, the required concurrency is 100 * 0.2 = 20, well within the default 1,000 concurrency limit. Option A is incorrect because reducing the batch size to 10 would increase invocations to 1,000 per second, requiring 200 concurrent executions, which still fits but is less efficient; the main issue is that reducing batch size does not reduce throttling risk as it increases invocation rate. Option B is incorrect because Kinesis Data Firehose does not natively support Lambda for per-record transformations before writing to DynamoDB; it primarily targets S3, Redshift, or Elasticsearch. Option C is incorrect because increasing Lambda memory typically reduces execution time but does not lower concurrency requirements; moreover, 3,000 MB may not reduce time below 100 ms enough to avoid throttling at high throughput.

What should I do if I get this MLS-C01 question wrong?

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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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.