Create an Azure Virtual Machine Scale Set
In this lab, you will create a Virtual Machine Scale Set in Azure with a custom scaling rule and use Spot instances to reduce costs.

Lab overview
Azure Virtual Machine Scale Sets are a resource type in Azure that allows you to create a group of identical, load-balanced VMs. They are a cost-effective way to scale your applications and services. You can create a scale set with a single VM or a large number of VMs to host your application.
This lab provides a basic understanding of Virtual Machine Scale Sets by helping you create a scale set and configure scaling rules.
Objectives
Upon completion of this beginner level lab, you will be able to:
- Create a Virtual Machine Scale Set
- Add a custom scaling rule to the scale set
- Use Spot instances to reduce costs
Who is this lab for?
- Azure administrators
- DevOps engineers
- Cloud engineers
Verified against your live environment
An automated validation engine inspects your actual resources and configurations as you work. Completion means the task was performed — not multiple choice, real-world proficiency.
More labs like this
Creating Your First Virtual Machine in Azure Cloud
This lab guides you through creating a virtual machine in Azure Cloud, covering essential steps and best practices for a successful deployment.
Deploy a Virtual Network and Virtual Machine Using Bicep
Build a real-world Bicep template that deploys a VNet with subnets, NSG rules, a public IP, and a Linux VM with SSH access.
Creating a Web App on Azure App Service using Azure Portal
Learn how to create, configure, and deploy a web application using Azure App Service through the Azure Portal's interface.
Related reading
Environment
Every lab includes
- Real environment, pre-credentialed
- Automated checks on every step
- Isolated sandbox, auto cleanup
- AI-recommended next steps
Lab curriculum
- 01
Logging into Azure Account using Azure Portal
- 02
Creating Azure Virtual Machine Scale Set using Azure Portal
1 automated check
Not the lab you were looking for?
Browse 150+ hands-on labs across AWS, Azure, Kubernetes, Docker, and cloud security.