MLS-C01 Modeling Practice Question
A company is deploying a machine learning model for real-time fraud detection. The model must have low latency (under 100 ms) and high throughput. The model is an ensemble of 5 gradient boosted trees (XGBoost), each 200 MB. Which deployment strategy is MOST suitable?
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 SageMaker multi-model endpoint on a compute-optimized instance.
A multi-model endpoint on a compute-optimized instance allows loading multiple models dynamically, reducing cost and latency compared to separate endpoints. Option A is wrong because invoking each model sequentially with Lambda would incur overhead and cold start latency, exceeding the 100 ms requirement. Option B is wrong because deploying each model as a separate SageMaker endpoint would require managing multiple endpoints, increasing cost and complexity without benefiting from model sharing on a single instance. Option C is wrong because batch processing is not suitable for real-time inference; GPU instances are overkill for tree-based models and add latency.
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 invoke each model sequentially.
Why it's wrong here
Invoking each model sequentially with Lambda would incur overhead and cold start latency, exceeding the 100 ms requirement.
- ✗
Deploy each model as a separate SageMaker endpoint and use a load balancer.
Why it's wrong here
Deploying each model as a separate SageMaker endpoint would require managing multiple endpoints, increasing cost and complexity without benefiting from model sharing on a single instance.
- ✗
Deploy the ensemble on a single GPU instance with large batch processing.
Why it's wrong here
Batch processing is not suitable for real-time inference; GPU instances are overkill for tree-based models and add latency.
- ✓
Use SageMaker multi-model endpoint on a compute-optimized instance.
Why this is correct
A multi-model endpoint on a compute-optimized instance allows loading multiple models dynamically, reducing cost and latency compared to separate endpoints.
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.