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MLA-C01 Practice Question: A machine learning engineer is building a…
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.
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.
- ✗
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.
- ✗
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.
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 |
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.