- A
Use AWS Glue ETL jobs running in streaming mode to read from Kinesis Data Streams, apply window aggregations, and write to S3.
Why wrong: Glue streaming ETL is available but has higher latency and cost; it is better suited for batch near-real-time than true real-time.
- B
Use Kinesis Data Streams with enhanced fan-out and multiple consumers to aggregate windows, then write to S3 via Firehose.
Why wrong: Enhanced fan-out improves throughput but does not provide built-in windowing; consumers must implement aggregation logic.
- C
Use Kinesis Data Streams, trigger a Lambda function for 1-minute window aggregation using Python, and write results to S3.
Why wrong: Lambda can process streams but has limits on concurrency and execution duration; managing window state is complex and error-prone.
- D
Use Kinesis Data Analytics for SQL-based windowed aggregations and send results to Kinesis Data Firehose for delivery to S3.
Kinesis Data Analytics supports tumbling windows and continuous queries; Firehose is the natural sink for S3.
MLS-C01 Data Engineering Practice Question
This MLS-C01 practice question tests your understanding of data engineering. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 data engineering team needs to process streaming data from thousands of IoT devices. They want to aggregate data in 1-minute windows and store results in an S3 data lake for downstream analytics. Which architecture should they use?
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
Use Kinesis Data Analytics for SQL-based windowed aggregations and send results to Kinesis Data Firehose for delivery to S3.
Option D is correct because Kinesis Data Analytics provides real-time SQL-based processing with windowing functions, and Kinesis Firehose can deliver aggregated data directly to S3. Option A is wrong because Lambda scales but has a 15-minute timeout and is not ideal for heavy streaming aggregation. Option B is wrong because Kinesis Data Streams alone does not process data; it requires a consumer. Option C is wrong because Glue is batch-oriented, not real-time.
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.
- ✗
Use AWS Glue ETL jobs running in streaming mode to read from Kinesis Data Streams, apply window aggregations, and write to S3.
Why it's wrong here
Glue streaming ETL is available but has higher latency and cost; it is better suited for batch near-real-time than true real-time.
- ✗
Use Kinesis Data Streams with enhanced fan-out and multiple consumers to aggregate windows, then write to S3 via Firehose.
Why it's wrong here
Enhanced fan-out improves throughput but does not provide built-in windowing; consumers must implement aggregation logic.
- ✗
Use Kinesis Data Streams, trigger a Lambda function for 1-minute window aggregation using Python, and write results to S3.
Why it's wrong here
Lambda can process streams but has limits on concurrency and execution duration; managing window state is complex and error-prone.
- ✓
Use Kinesis Data Analytics for SQL-based windowed aggregations and send results to Kinesis Data Firehose for delivery to S3.
Why this is correct
Kinesis Data Analytics supports tumbling windows and continuous queries; Firehose is the natural sink for S3.
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.
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: Use Kinesis Data Analytics for SQL-based windowed aggregations and send results to Kinesis Data Firehose for delivery to S3. — Option D is correct because Kinesis Data Analytics provides real-time SQL-based processing with windowing functions, and Kinesis Firehose can deliver aggregated data directly to S3. Option A is wrong because Lambda scales but has a 15-minute timeout and is not ideal for heavy streaming aggregation. Option B is wrong because Kinesis Data Streams alone does not process data; it requires a consumer. Option C is wrong because Glue is batch-oriented, not real-time.
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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Last reviewed: Jun 20, 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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