- A
Use Amazon Kinesis Data Analytics for Apache Flink to consume the stream, perform feature engineering, and invoke the SageMaker endpoint with exactly-once processing.
Kinesis Data Analytics provides stateful stream processing with checkpointing, ensuring no data loss and low-latency integration with SageMaker.
- B
Use the Kinesis Client Library (KCL) to process the stream in an Amazon EC2 instance, and store the predictions in Amazon DynamoDB.
Why wrong: KCL-based processing on EC2 adds operational overhead and does not directly address Lambda timeout issues; DynamoDB storage is not necessary for the pipeline.
- C
Increase the Lambda function timeout to 15 minutes and allocate more memory to reduce processing time.
Why wrong: Lambda has a maximum timeout of 15 minutes, but this increases cost and still risks data loss if failures occur; it does not provide checkpointing.
- D
Configure Amazon Kinesis Firehose to deliver the stream to an Amazon S3 bucket, then trigger a Lambda function to process the data in batches.
Why wrong: Kinesis Firehose introduces minutes of delay, which is unsuitable for real-time fraud detection.
Quick Answer
The answer is to use Amazon Kinesis Data Analytics for Apache Flink to consume the stream, perform feature engineering, and invoke the SageMaker endpoint with exactly-once processing. This is correct because Apache Flink provides a stateful, long-running stream processing engine that eliminates the timeout and data loss issues inherent in short-lived Lambda functions, enabling reliable, low-latency real-time ML inference on streaming data. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of choosing the right streaming compute service for stateful, exactly-once processing versus stateless, ephemeral functions like Lambda, which are a common trap for simple tasks but fail under sustained throughput. A key memory tip: think “Flink for stateful streaming, Lambda for lightweight triggers”—if the task requires maintaining state or invoking ML models on every record without timeouts, Flink is the durable choice.
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 science team is building a real-time fraud detection system. Transactions are streamed via Amazon Kinesis Data Streams, and a Lambda function performs feature engineering and invokes an Amazon SageMaker endpoint for predictions. The team notices that the Lambda function is timing out and causing data loss. Which solution should the team implement to process the stream reliably and at low latency?
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 consume the stream, perform feature engineering, and invoke the SageMaker endpoint with exactly-once processing.
Option A is correct because Amazon Kinesis Data Analytics for Apache Flink provides a stateful, low-latency stream processing engine that can consume from Kinesis Data Streams, perform feature engineering in real-time, and invoke SageMaker endpoints with exactly-once processing semantics. This eliminates Lambda timeouts and data loss by using a long-running, scalable application instead of a short-lived function.
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 Amazon Kinesis Data Analytics for Apache Flink to consume the stream, perform feature engineering, and invoke the SageMaker endpoint with exactly-once processing.
Why this is correct
Kinesis Data Analytics provides stateful stream processing with checkpointing, ensuring no data loss and low-latency integration with SageMaker.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use the Kinesis Client Library (KCL) to process the stream in an Amazon EC2 instance, and store the predictions in Amazon DynamoDB.
Why it's wrong here
KCL-based processing on EC2 adds operational overhead and does not directly address Lambda timeout issues; DynamoDB storage is not necessary for the pipeline.
- ✗
Increase the Lambda function timeout to 15 minutes and allocate more memory to reduce processing time.
Why it's wrong here
Lambda has a maximum timeout of 15 minutes, but this increases cost and still risks data loss if failures occur; it does not provide checkpointing.
- ✗
Configure Amazon Kinesis Firehose to deliver the stream to an Amazon S3 bucket, then trigger a Lambda function to process the data in batches.
Why it's wrong here
Kinesis Firehose introduces minutes of delay, which is unsuitable for real-time fraud detection.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume increasing Lambda resources (timeout/memory) or moving to a batch-based approach (Firehose/S3) can solve real-time streaming issues, but the exam tests the understanding that stateful, long-running stream processing engines like Flink are required for reliable, low-latency, exactly-once processing in production.
Detailed technical explanation
How to think about this question
Apache Flink on Kinesis Data Analytics uses a checkpointing mechanism based on the Chandy-Lamport algorithm to achieve exactly-once processing, ensuring no data loss even during failures. The Flink application can maintain state (e.g., sliding windows for feature aggregation) and invoke SageMaker endpoints via HTTP requests with low millisecond latency, making it ideal for real-time fraud detection where sub-second response times are critical.
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.
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
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
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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 consume the stream, perform feature engineering, and invoke the SageMaker endpoint with exactly-once processing. — Option A is correct because Amazon Kinesis Data Analytics for Apache Flink provides a stateful, low-latency stream processing engine that can consume from Kinesis Data Streams, perform feature engineering in real-time, and invoke SageMaker endpoints with exactly-once processing semantics. This eliminates Lambda timeouts and data loss by using a long-running, scalable application instead of a short-lived function.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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 →
Same concept, more angles
1 more ways this is tested on MLS-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. 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?
medium- A.Use AWS Lambda to read from Kinesis, invoke SageMaker, and write directly to S3.
- B.Use Amazon Kinesis Data Firehose with a transformation Lambda to call SageMaker.
- ✓ C.Use Amazon Kinesis Data Analytics for Apache Flink to enrich records with SageMaker predictions, then output to Firehose for S3.
- D.Use AWS Lambda to invoke the SageMaker endpoint for each record, then write to S3 via Firehose.
Why C: 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.
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Last reviewed: Jun 11, 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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