MLS-C01 Modeling Practice Question
A machine learning team is deploying a model for real-time fraud detection. The model must make predictions with less than 100ms latency. The team uses SageMaker and the model is a large ensemble of decision trees. Which SageMaker hosting option is MOST suitable?
⚠ Common exam trap
Candidates often select SageMaker Multi-model endpoint (Option A) thinking it is the only real-time option, but it is designed for hosting multiple independent models, not a single large ensemble. A regular real-time endpoint or Serverless Inference is more appropriate. Serverless avoids the overhead of managing instances and can achieve low latency when the endpoint is continuously invoked.
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
✓
SageMaker Serverless Inference
SageMaker Serverless Inference is the most suitable option because it automatically scales to handle variable traffic and does not require managing underlying infrastructure. Although it may incur cold starts, for a constantly invoked fraud detection model the endpoint remains warm, achieving sub-100ms latency. The large ensemble of decision trees can be deployed as a single model on a Serverless endpoint, which is optimized for real-time inference with low latency and automatic scaling.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
SageMaker Multi-model endpoint
Why it's wrong here
SageMaker Multi-model endpoints are designed for cost-optimisation when hosting numerous distinct models that are infrequently invoked, by dynamically loading them onto shared instances. This dynamic loading mechanism introduces latency overhead, making it unsuitable for the strict sub-100ms real-time prediction requirement of a single, high-throughput model like a large decision tree ensemble. It is tempting as 'ensemble' implies multiple components, but it is for independent models, not sub-components of one logical model.
- ✓
SageMaker Serverless Inference
Why this is correct
Correct. SageMaker Serverless Inference provides automatic scaling and is ideal for real-time inference with low latency. For a constantly used model, cold starts are minimal, and the service handles the large ensemble efficiently.
- ✗
SageMaker Elastic Inference
Why it's wrong here
Incorrect. SageMaker Elastic Inference is designed for deep learning models, not decision tree ensembles. It is not appropriate for this scenario.
- ✗
SageMaker Batch Transform
Why it's wrong here
Incorrect. SageMaker Batch Transform is for offline, batch inference, not real-time predictions under 100ms.
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 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.