- A
Use AWS Lambda to read from Kinesis, invoke SageMaker, and write directly to S3.
Why wrong: Lambda is not optimized for high-frequency real-time scoring; may cause throttling.
- B
Use Amazon Kinesis Data Firehose with a transformation Lambda to call SageMaker.
Why wrong: Firehose transformation Lambda has a 5-minute timeout and limited concurrency, not ideal for real-time scoring.
- C
Use Amazon Kinesis Data Analytics for Apache Flink to enrich records with SageMaker predictions, then output to Firehose for S3.
Flink can handle high-throughput, call SageMaker per record, and output to Firehose.
- D
Use AWS Lambda to invoke the SageMaker endpoint for each record, then write to S3 via Firehose.
Why wrong: Lambda concurrency limits may throttle under high throughput.
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 engineering team is building a real-time fraud detection system. Transactions are ingested via Amazon Kinesis Data Streams, and a machine learning model (deployed on Amazon SageMaker) scores each transaction. The team needs to store the raw transactions and the model's predictions in Amazon S3 for later analysis. Which architecture should the team 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 Amazon Kinesis Data Analytics for Apache Flink to enrich records with SageMaker predictions, then output to Firehose for S3.
Option C is correct. Use Kinesis Data Analytics with a Flink application to enrich each record with the SageMaker prediction, then output to Kinesis Data Firehose for delivery to S3. Option A is wrong because Lambda cannot directly invoke SageMaker for every record in high-throughput streams due to concurrency limits. Option B is wrong because Kinesis Data Firehose does not support invoking SageMaker directly. Option D is wrong because Lambda is not suitable for high-frequency real-time scoring.
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 Lambda to read from Kinesis, invoke SageMaker, and write directly to S3.
Why it's wrong here
Lambda is not optimized for high-frequency real-time scoring; may cause throttling.
- ✗
Use Amazon Kinesis Data Firehose with a transformation Lambda to call SageMaker.
Why it's wrong here
Firehose transformation Lambda has a 5-minute timeout and limited concurrency, not ideal for real-time scoring.
- ✓
Use Amazon Kinesis Data Analytics for Apache Flink to enrich records with SageMaker predictions, then output to Firehose for S3.
Why this is correct
Flink can handle high-throughput, call SageMaker per record, and output to Firehose.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use AWS Lambda to invoke the SageMaker endpoint for each record, then write to S3 via Firehose.
Why it's wrong here
Lambda concurrency limits may throttle under high throughput.
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 Amazon Kinesis Data Analytics for Apache Flink to enrich records with SageMaker predictions, then output to Firehose for S3. — Option C is correct. Use Kinesis Data Analytics with a Flink application to enrich each record with the SageMaker prediction, then output to Kinesis Data Firehose for delivery to S3. Option A is wrong because Lambda cannot directly invoke SageMaker for every record in high-throughput streams due to concurrency limits. Option B is wrong because Kinesis Data Firehose does not support invoking SageMaker directly. Option D is wrong because Lambda is not suitable for high-frequency real-time scoring.
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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