- 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.
Building a Real-Time Fraud Detection Pipeline with Kinesis and SageMaker
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 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 because it uses Amazon Kinesis Data Analytics for Apache Flink to perform real-time enrichment by invoking the SageMaker endpoint for each transaction, then streams the enriched records to Kinesis Data Firehose for reliable, batched delivery to S3. This architecture handles the asynchronous nature of model inference without blocking the ingestion stream, and Firehose provides automatic retry and compression for S3 storage.
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
The trap here is that candidates often assume Lambda is the only serverless option for real-time enrichment, but the exam tests whether you understand that Kinesis Data Analytics for Apache Flink is the correct service for asynchronous, stateful enrichment before delivery to S3 via Firehose.
Detailed technical explanation
How to think about this question
Kinesis Data Analytics for Apache Flink uses a Flink application that can maintain state and perform per-record operations like calling a SageMaker endpoint via HTTP. The enriched records are then emitted to a Firehose delivery stream, which buffers them (up to 128 MB or 60 seconds) before writing to S3 in Parquet or ORC format. This pattern is critical for fraud detection where low-latency scoring is needed without losing the ability to batch-write to S3 for cost efficiency.
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.
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
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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 because it uses Amazon Kinesis Data Analytics for Apache Flink to perform real-time enrichment by invoking the SageMaker endpoint for each transaction, then streams the enriched records to Kinesis Data Firehose for reliable, batched delivery to S3. This architecture handles the asynchronous nature of model inference without blocking the ingestion stream, and Firehose provides automatic retry and compression for S3 storage.
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 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?
medium- ✓ 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.
- B.Use the Kinesis Client Library (KCL) to process the stream in an Amazon EC2 instance, and store the predictions in Amazon DynamoDB.
- C.Increase the Lambda function timeout to 15 minutes and allocate more memory to reduce processing time.
- 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 A: 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.
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Last reviewed: Jul 4, 2026
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