- A
Use Kinesis Data Analytics to write output to S3.
Correct. Kinesis Data Analytics (Flink) can read raw data from the stream and write it to S3 with a customizable window, such as every hour, serving as an archival mechanism.
- B
Configure a Lambda function as a consumer of the stream for real-time processing.
Correct. Lambda as a stream consumer provides real-time processing with sub-second latency, meeting the latency requirement.
- C
Use S3 events to trigger a Lambda function that reads from the stream.
Why wrong: Incorrect. S3 events are triggered after data is already in S3, not for real-time stream consumption; this would not meet the sub-second latency requirement for processing.
- D
Create a Kinesis Data Firehose delivery stream with S3 as destination and set a buffer interval of 3600 seconds.
Why wrong: Incorrect. Kinesis Data Firehose supports a maximum buffer interval of 900 seconds; setting it to 3600 seconds is not feasible, so it cannot archive every hour as stated.
- E
Install the Kinesis Agent on an EC2 instance to write data to S3.
Why wrong: Incorrect. The Kinesis Agent is designed to push data from EC2 into Kinesis streams, not to read from streams or write to S3 directly.
Set Up Real-Time Processing and Hourly Archiving with Kinesis
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. A key principle to apply: kinesis Data Streams consumer. 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 uses Amazon Kinesis Data Streams to ingest clickstream data. They need to archive raw data to S3 every hour and also enable real-time processing with sub-second latency. Which TWO actions should they take? (Choose two.)
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 to write output to S3.
Option B is correct because AWS Lambda can be configured as a consumer of a Kinesis Data Stream using event source mapping, enabling real-time processing with sub-second latency. Option A is correct because Kinesis Data Analytics (now Amazon Managed Service for Apache Flink) can read from a Kinesis stream and write output to S3, making it suitable for archiving raw data every hour—e.g., using a Flink sink with a tumbling window of 1 hour. Option D is incorrect because Kinesis Data Firehose has a maximum buffer interval of 900 seconds (15 minutes), not 3600 seconds; thus it cannot archive data exactly every hour as specified. Option C is incorrect because S3 events trigger Lambda on object creation in S3, not on real-time stream data. Option E is incorrect because the Kinesis Agent is used to send data from EC2 to Kinesis, not to S3 directly.
Key principle: Kinesis Data Streams consumer
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 Kinesis Data Analytics to write output to S3.
Why this is correct
Correct. Kinesis Data Analytics (Flink) can read raw data from the stream and write it to S3 with a customizable window, such as every hour, serving as an archival mechanism.
Related concept
Kinesis Data Streams consumer
- ✓
Configure a Lambda function as a consumer of the stream for real-time processing.
Why this is correct
Correct. Lambda as a stream consumer provides real-time processing with sub-second latency, meeting the latency requirement.
Related concept
Kinesis Data Streams consumer
- ✗
Use S3 events to trigger a Lambda function that reads from the stream.
Why it's wrong here
Incorrect. S3 events are triggered after data is already in S3, not for real-time stream consumption; this would not meet the sub-second latency requirement for processing.
- ✗
Create a Kinesis Data Firehose delivery stream with S3 as destination and set a buffer interval of 3600 seconds.
Why it's wrong here
Incorrect. Kinesis Data Firehose supports a maximum buffer interval of 900 seconds; setting it to 3600 seconds is not feasible, so it cannot archive every hour as stated.
- ✗
Install the Kinesis Agent on an EC2 instance to write data to S3.
Why it's wrong here
Incorrect. The Kinesis Agent is designed to push data from EC2 into Kinesis streams, not to read from streams or write to S3 directly.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates often overlook that Kinesis Data Firehose has a maximum buffer interval of 900 seconds, making it unsuitable for hourly archives, and they may also underestimate Kinesis Data Analytics (Flink) as an archiving solution, assuming it only processes data rather than writing raw data to S3.
Detailed technical explanation
How to think about this question
Kinesis Data Firehose (Option D) is the correct service for archiving raw data to S3 with a configurable buffer interval (up to 900 seconds, but 3600 seconds is achievable by setting a buffer size or using a Lambda transformation to enforce hourly delivery). Under the hood, Firehose batches records based on buffer size (default 5 MB) or buffer interval (up to 900 seconds), but you can use a custom Lambda function to flush data every hour by monitoring the `approximateArrivalTimestamp` of records. In real-world scenarios, combining Lambda for real-time processing and Firehose for batch archiving is a common pattern for clickstream pipelines.
KKey Concepts to Remember
- Kinesis Data Streams consumer
- Kinesis Data Analytics (Flink) S3 sink
- Kinesis Data Firehose buffer limits
- Real-time vs. batch processing
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
Kinesis Data Streams consumer
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.
Review kinesis Data Streams consumer, then practise related MLS-C01 questions on the same topic to reinforce the concept.
- →
Data Engineering — study guide chapter
Learn the concepts, then practise the questions
- →
Data Engineering practice questions
Targeted practice on this topic area only
- →
All MLS-C01 questions
1,755 questions across all exam domains
- →
AWS Certified Machine Learning Specialty MLS-C01 study guide
Full concept coverage aligned to exam objectives
- →
MLS-C01 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related MLS-C01 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Data Engineering practice questions
Practise MLS-C01 questions linked to Data Engineering.
Machine Learning Implementation and Operations practice questions
Practise MLS-C01 questions linked to Machine Learning Implementation and Operations.
Modeling practice questions
Practise MLS-C01 questions linked to Modeling.
Exploratory Data Analysis practice questions
Practise MLS-C01 questions linked to Exploratory Data Analysis.
MLS-C01 fundamentals practice questions
Practise MLS-C01 questions linked to MLS-C01 fundamentals.
MLS-C01 scenario practice questions
Practise MLS-C01 questions linked to MLS-C01 scenario.
MLS-C01 troubleshooting practice questions
Practise MLS-C01 questions linked to MLS-C01 troubleshooting.
Practice this exam
Start a free MLS-C01 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Questions learners often ask
What does this MLS-C01 question test?
Data Engineering — This question tests Data Engineering — Kinesis Data Streams consumer.
What is the correct answer to this question?
The correct answer is: Use Kinesis Data Analytics to write output to S3. — Option B is correct because AWS Lambda can be configured as a consumer of a Kinesis Data Stream using event source mapping, enabling real-time processing with sub-second latency. Option A is correct because Kinesis Data Analytics (now Amazon Managed Service for Apache Flink) can read from a Kinesis stream and write output to S3, making it suitable for archiving raw data every hour—e.g., using a Flink sink with a tumbling window of 1 hour. Option D is incorrect because Kinesis Data Firehose has a maximum buffer interval of 900 seconds (15 minutes), not 3600 seconds; thus it cannot archive data exactly every hour as specified. Option C is incorrect because S3 events trigger Lambda on object creation in S3, not on real-time stream data. Option E is incorrect because the Kinesis Agent is used to send data from EC2 to Kinesis, not to S3 directly.
What should I do if I get this MLS-C01 question wrong?
Review kinesis Data Streams consumer, then practise related MLS-C01 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Kinesis Data Streams consumer
About these practice questions
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 →
Keep practising
More MLS-C01 practice questions
- A company needs to transfer 10 TB of data from an on-premises data center to Amazon S3. The network bandwidth is limited…
- A company is using Amazon Kinesis Data Streams to ingest real-time clickstream data. The data is consumed by a Lambda fu…
- A team is building a data pipeline to process terabytes of log data daily using Amazon EMR. The data arrives in 5-minute…
- A data science team is building a real-time fraud detection system. Transactions are streamed via Amazon Kinesis Data St…
- A company uses Amazon SageMaker to train and deploy machine learning models. The training data is stored in Amazon S3 (P…
- A data engineering team is designing a data lake on AWS for machine learning workloads. The data includes structured, se…
Last reviewed: Jul 4, 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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.