MLS-C01 Practice Question: Machine Learning Implementation and Operations
A machine learning engineer needs to deploy a model that performs real-time fraud detection. The model must be highly available and scalable. Which AWS service should be used to host the model?
⚠ Common exam trap
Watch out — candidates often confuse batch transform with real-time inference, or assume Lambda can handle persistent, low-latency model serving without considering its timeout and payload size limits.
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
✓
Amazon SageMaker real-time endpoint
Amazon SageMaker real-time endpoints are designed for low-latency, synchronous inference, making them ideal for real-time fraud detection. They automatically scale across multiple instances and Availability Zones, providing high availability and elasticity to handle variable traffic loads without manual intervention.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
AWS Lambda
Why it's wrong here
Lambda has time and memory limits.
- ✗
Amazon ECS with a custom container
Why it's wrong here
Possible but requires manual scaling and monitoring.
- ✗
Amazon SageMaker batch transform
Why it's wrong here
Not real-time.
- ✓
Amazon SageMaker real-time endpoint
Why this is correct
Purpose-built for real-time inference with auto-scaling.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.