- A
Use AWS Lambda functions to apply feature transformations on each record as it arrives.
Lambda can run custom code (e.g., Python) with low latency on each Kinesis record.
- B
Use SageMaker batch transform jobs scheduled every hour to process the streaming data.
Why wrong: Batch transform is not real-time; it processes data in bulk with latency of hours.
- C
Use AWS Glue ETL jobs running on a recurring schedule to transform the data.
Why wrong: Glue ETL jobs are batch-oriented and introduce significant latency, unsuitable for real-time.
- D
Use Amazon Kinesis Data Analytics to perform SQL-based transformations on the stream.
Kinesis Data Analytics can apply SQL queries to streaming data with low latency.
- E
Use SageMaker Processing jobs to read from Kinesis and write transformed data to S3.
Why wrong: Processing jobs are for offline, not real-time, processing.
MLA-C01 Practice Question: A machine learning engineer is building a…
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 machine learning engineer is building a real-time fraud detection pipeline using Amazon Kinesis Data Streams. The data must be prepared (e.g., feature engineering, normalization) before being fed into a SageMaker endpoint. Which TWO steps should the engineer implement to ensure low-latency data preparation?
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 AWS Lambda functions to apply feature transformations on each record as it arrives.
AWS Lambda can process records from Kinesis in near real-time for lightweight transformations like normalization and feature engineering. For streaming data, SageMaker batch transform is not real-time. Glue ETL is batch-oriented and adds latency. Amazon Kinesis Data Analytics can perform SQL-based transformations in real-time. SageMaker Processing jobs are designed for offline processing.
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 functions to apply feature transformations on each record as it arrives.
Why this is correct
Lambda can run custom code (e.g., Python) with low latency on each Kinesis record.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use SageMaker batch transform jobs scheduled every hour to process the streaming data.
Why it's wrong here
Batch transform is not real-time; it processes data in bulk with latency of hours.
- ✗
Use AWS Glue ETL jobs running on a recurring schedule to transform the data.
Why it's wrong here
Glue ETL jobs are batch-oriented and introduce significant latency, unsuitable for real-time.
- ✓
Use Amazon Kinesis Data Analytics to perform SQL-based transformations on the stream.
Why this is correct
Kinesis Data Analytics can apply SQL queries to streaming data with low latency.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use SageMaker Processing jobs to read from Kinesis and write transformed data to S3.
Why it's wrong here
Processing jobs are for offline, not real-time, processing.
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
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
What to study next
Got this wrong? Here's your next step.
Identify which MLA-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 MLA-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Use AWS Lambda functions to apply feature transformations on each record as it arrives. — AWS Lambda can process records from Kinesis in near real-time for lightweight transformations like normalization and feature engineering. For streaming data, SageMaker batch transform is not real-time. Glue ETL is batch-oriented and adds latency. Amazon Kinesis Data Analytics can perform SQL-based transformations in real-time. SageMaker Processing jobs are designed for offline processing.
What should I do if I get this MLA-C01 question wrong?
Identify which MLA-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: Jul 4, 2026
This MLA-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 MLA-C01 exam.
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