- A
Amazon Kinesis Data Streams for ingestion, Amazon Kinesis Data Analytics for windowed aggregations, and Amazon Kinesis Data Firehose to write to Amazon S3.
Kinesis Data Analytics supports windowed aggregations and exactly-once processing; Firehose delivers to S3 with minimal overhead.
- B
Amazon Kinesis Data Streams for ingestion, AWS Glue Streaming ETL for aggregation, and Amazon S3 for storage.
Why wrong: Glue Streaming is a batch-oriented service with higher latency; not ideal for real-time streaming.
- C
Amazon SQS for ingestion, AWS Lambda for aggregation, and Amazon S3 for storage.
Why wrong: SQS is a message queue, not a streaming platform; Lambda does not provide exactly-once semantics for streaming data.
- D
Amazon Kinesis Data Streams for ingestion, Amazon Kinesis Data Firehose for transformation, and Amazon S3 for storage.
Why wrong: Firehose does not support custom windowed aggregations; it delivers data as-is or with simple transformations.
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 data engineer is tasked with building a pipeline to process streaming data from IoT devices. The devices send data in JSON format every second. The pipeline must aggregate data in 5-minute windows and store the results in Amazon S3. The engineer needs to handle late-arriving data (up to 1 hour) and ensure exactly-once semantics. Which combination of AWS services 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
Amazon Kinesis Data Streams for ingestion, Amazon Kinesis Data Analytics for windowed aggregations, and Amazon Kinesis Data Firehose to write to Amazon S3.
Option A is correct because Amazon Kinesis Data Analytics supports windowed aggregations via SQL or Apache Flink, can handle late-arriving data using watermarking, and when used with Kinesis Data Streams as the source, provides exactly-once processing semantics. Option B is incorrect because AWS Glue Streaming ETL is based on Spark Streaming; while it can perform windowed aggregations, it does not natively guarantee exactly-once semantics with Kinesis Data Streams and is more complex to configure for late data handling. Option C is incorrect because Amazon SQS is not designed for high-throughput streaming ingestion (IoT devices sending data every second), and AWS Lambda invocations are at-least-once, not exactly-once, making it unsuitable for the required exactly-once semantics. Option D is incorrect because Amazon Kinesis Data Firehose is a delivery service that does not support custom windowed aggregations; it delivers data after a minimum buffer interval of 60 seconds and cannot handle late-arriving data with exactly-once guarantees.
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.
- ✓
Amazon Kinesis Data Streams for ingestion, Amazon Kinesis Data Analytics for windowed aggregations, and Amazon Kinesis Data Firehose to write to Amazon S3.
Why this is correct
Kinesis Data Analytics supports windowed aggregations and exactly-once processing; Firehose delivers to S3 with minimal overhead.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Amazon Kinesis Data Streams for ingestion, AWS Glue Streaming ETL for aggregation, and Amazon S3 for storage.
Why it's wrong here
Glue Streaming is a batch-oriented service with higher latency; not ideal for real-time streaming.
- ✗
Amazon SQS for ingestion, AWS Lambda for aggregation, and Amazon S3 for storage.
Why it's wrong here
SQS is a message queue, not a streaming platform; Lambda does not provide exactly-once semantics for streaming data.
- ✗
Amazon Kinesis Data Streams for ingestion, Amazon Kinesis Data Firehose for transformation, and Amazon S3 for storage.
Why it's wrong here
Firehose does not support custom windowed aggregations; it delivers data as-is or with simple transformations.
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.
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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: Amazon Kinesis Data Streams for ingestion, Amazon Kinesis Data Analytics for windowed aggregations, and Amazon Kinesis Data Firehose to write to Amazon S3. — Option A is correct because Amazon Kinesis Data Analytics supports windowed aggregations via SQL or Apache Flink, can handle late-arriving data using watermarking, and when used with Kinesis Data Streams as the source, provides exactly-once processing semantics. Option B is incorrect because AWS Glue Streaming ETL is based on Spark Streaming; while it can perform windowed aggregations, it does not natively guarantee exactly-once semantics with Kinesis Data Streams and is more complex to configure for late data handling. Option C is incorrect because Amazon SQS is not designed for high-throughput streaming ingestion (IoT devices sending data every second), and AWS Lambda invocations are at-least-once, not exactly-once, making it unsuitable for the required exactly-once semantics. Option D is incorrect because Amazon Kinesis Data Firehose is a delivery service that does not support custom windowed aggregations; it delivers data after a minimum buffer interval of 60 seconds and cannot handle late-arriving data with exactly-once guarantees.
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.
About these practice questions
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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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