# VM scale set

> Source: Courseiva IT Certification Glossary — https://courseiva.com/glossary/vm-scale-set

## Quick definition

A VM scale set lets you run a group of identical virtual machines that automatically grow or shrink based on how much work there is. For example, if your website gets more visitors, the scale set adds more VMs to handle the traffic. When traffic drops, it removes the extra VMs to save money and resources. It keeps your application running smoothly without you having to manually add or remove servers.

## Simple meaning

Imagine you run a lemonade stand. On a hot day, a huge crowd shows up. You cannot serve everyone fast enough with just one pitcher. So you quickly set up three more tables with their own pitchers and cups. When the crowd leaves, you pack up the extra tables and go back to just one. A VM scale set does something similar, but for computer servers that run websites or apps.

A virtual machine, or VM, is like a pretend computer that lives in the cloud. You can run programs on it, just like on a real computer. A VM scale set is a collection of these pretend computers that are all set up exactly the same way. They all run the same software and do the same job.

The clever part is that the scale set watches how much work is coming in. If lots of people are using a website, the scale set automatically creates more VMs to share the load. Each new VM is identical to the others, so the website keeps running properly. This is called scaling out.

When the work dies down, the scale set removes the extra VMs. This is called scaling in. You only pay for the VMs that are actually running, so scaling in saves money.

You can tell the scale set when to add or remove VMs. For example, you might say If the CPU usage goes above 80 percent for five minutes, add two more VMs. Or you might schedule it to add more VMs during your busiest hours, like lunchtime if you run a food delivery app.

The best part is that this all happens automatically. You do not have to sit in front of a computer waiting to add servers. The scale set does it for you, based on rules you set up ahead of time. This is a key part of cloud computing, because it lets applications handle unpredictable traffic without wasting money on idle servers.

## Technical definition

A VM scale set is an Azure resource that provides a declarative way to deploy and manage a group of identical, load-balanced virtual machines. Although the term VM scale set is most commonly associated with Microsoft Azure, similar concepts exist in other cloud providers, such as Auto Scaling Groups in AWS and Instance Groups in Google Cloud. In Azure, a VM scale set is built on top of Azure Virtual Machines and integrates with the Azure Load Balancer or Application Gateway to distribute traffic across the instances.

At its core, a VM scale set uses a virtual machine configuration template. This template defines the VM size, the operating system image, disk configuration, network settings, and any extensions or custom scripts to install. When the scale set scales out, it creates new VMs from this exact template, ensuring all instances are identical. This is crucial for stateless applications, where any instance can handle any request.

The autoscaling mechanism relies on metrics collected by Azure Monitor. Common metrics include average CPU utilization, memory pressure, disk queue depth, and incoming HTTP request count. You define autoscale rules that specify a metric, a threshold, a duration, and an action. For example, a rule might state When average CPU > 75% for 10 minutes, increase instance count by 2, up to a maximum of 20 instances. The scale set also supports scale-in rules to reduce capacity when demand drops.

VM scale sets support both manual and automatic scaling modes. In manual mode, you directly set the desired instance count. In automatic mode, the system evaluates the rules continuously. There is also a scheduled scaling option, which allows you to set a specific instance count at certain times of the day or week. This is useful for predictable workloads, such as scaling up during business hours and down at night.

Under the hood, VM scale sets use a fault domain and update domain structure to maintain high availability. Fault domains group VMs by physical hardware, so a single rack or power failure does not take down all instances. Update domains ensure that when you update the VM configuration, only a subset of VMs is updated at a time, minimizing downtime.

One important feature is instance protection. You can mark certain instances to be excluded from scale-in events, which is useful for instances that are performing special tasks like running a batch job. Also, VM scale sets support overprovisioning, which means the system may create extra VMs during scale-out to ensure that enough healthy instances are available even if some fail during provisioning.

Real IT implementations use VM scale sets for stateless web tiers, microservices, batch processing, and high-performance computing. They are also used with Azure Kubernetes Service (AKS) as node pools, where the cluster automatically scales the underlying VMs based on pod resource demands.

Finally, VM scale sets are charged only for the underlying VM instances and any associated resources, such as storage disks and load balancer. The scale set resource itself does not incur a separate charge. This makes it a cost-effective way to handle variable workloads in production environments.

## Real-life example

Think about a busy restaurant kitchen. During a normal evening, there is one chef cooking at the stove. Orders come in steadily, and the chef handles them one by one. But on Friday night, a huge rush of customers arrives. Suddenly, orders are piling up, and the chef cannot keep up. The restaurant manager sees this and calls in two more chefs from the back. They set up two extra cooking stations with the same ingredients and recipes. Now all three chefs work together, each making part of the orders. The food comes out quickly, and customers are happy. After the rush, the manager sends the extra chefs home, and the kitchen goes back to one chef.

In this analogy, the chef and the cooking station together represent one virtual machine. The restaurant manager is the autoscaling system. The manager watches a key metric: the number of pending orders. When that number gets too high, the manager adds more chefs. When it drops, the manager removes them. The recipes and ingredients are the configuration template that makes sure every chef cooks the same way, so the food is consistent no matter who makes it.

Notice that the extra chefs and stations are identical. They use the same ingredients, the same pans, and the same recipes. That is exactly how a VM scale set works: every VM is a clone of the same configuration. Also, the manager does not add chefs randomly. They have a rule: If pending orders are more than ten, add one chef. This rule is like the autoscale rule you set in Azure.

If the restaurant were a modern cloud application, adding more chefs instantly would be impossible in real life because you have to hire them first. But in the cloud, you can create a new VM in minutes. That speed is what makes VM scale sets so powerful. You can respond to demand surges almost instantly, without overspending on idle capacity during quiet times.

## Why it matters

VM scale sets matter because they solve one of the most fundamental problems in IT operations: matching capacity to demand. In traditional on-premises environments, you had to buy enough servers to handle peak load, which meant most servers sat idle most of the time. That wasted money on hardware, electricity, cooling, and maintenance. With cloud computing, you can pay only for what you use. But without automated scaling, you would still need to manually monitor and adjust servers, which is error-prone and labor-intensive.

For IT professionals, VM scale sets provide a reliable, automated way to ensure application availability and performance during traffic spikes. Whether you are running an e-commerce site that gets hammered on Black Friday, a streaming service that sees evening surges, or a data processing pipeline that runs batch jobs overnight, a scale set can handle the variability. It also improves resilience. If a VM fails, the scale set can automatically replace it, keeping the service healthy.

From a cost perspective, scaling in during low demand directly reduces your cloud bill. You are not paying for servers that are doing nothing. Many organizations have saved 30 to 50 percent on compute costs by implementing autoscaling for variable workloads. At the same time, you avoid the risk of under-provisioning, which would cause slow performance or downtime during busy periods.

Security and compliance also benefit. Because all VMs in a scale set are identical, you can enforce a consistent security baseline. If a vulnerability is discovered, you can update the VM configuration template and roll out the fix to all instances uniformly. There is no drift between servers, which is a common problem in manually managed environments.

Finally, knowledge of VM scale sets is directly applicable to modern DevOps and cloud-native architectures. They are a building block for container orchestration platforms like Kubernetes, which often use auto-scaling groups at the node level. Understanding how scale sets work gives you a deeper appreciation for how cloud platforms achieve elasticity at scale. For IT certification learners, this concept appears in exams such as Azure Administrator (AZ-104), Azure Solutions Architect (AZ-305), and AWS Certified Solutions Architect, so it is not just a nice-to-have but a required skill.

## Why it matters in exams

VM scale sets are a core topic in several major cloud certification exams, particularly in the Microsoft Azure track. In the Azure Administrator exam (AZ-104), one of the primary objectives is Implement and manage virtual machines, and a sub-objective specifically covers deploying and configuring Azure Virtual Machine Scale Sets. You can expect questions about creating a scale set from the portal, using ARM templates, configuring autoscale rules, and integrating with Azure Load Balancer. The exam tests not only the theory but also practical knowledge of settings, such as the difference between automatic and manual scaling, and how to configure metric-based rules.

In the Azure Solutions Architect exam (AZ-305), VM scale sets appear in the context of designing for high availability and scalability. You need to know when to recommend a scale set versus individual VMs, how to align scale sets with availability zones, and how to use scale sets in multi-region architectures. The exam may present a scenario with a stateless web application and ask you to design a solution that can handle fluctuating traffic while minimizing cost. The correct answer will often involve a VM scale set with autoscaling rules based on CPU or queue depth.

The AWS Certified Solutions Architect exam covers the equivalent concept under Auto Scaling Groups (ASG). Although the term VM scale set is Azure-specific, the fundamental principles are identical. If you are studying for AWS exams, you will need to understand launch configurations, scaling policies, and how ASGs work with Elastic Load Balancers. Similarly, the Google Cloud Professional Cloud Architect exam covers managed instance groups with autoscaling.

For general IT certifications like CompTIA Cloud+, the concept appears at a higher level. You are expected to understand the benefits of elastic scaling, the difference between vertical and horizontal scaling, and the role of load balancers. You do not need to memorize Azure-specific commands, but you should be able to explain how a scale set provides fault tolerance and cost efficiency.

Exam questions about VM scale sets often take one of three forms: choosing the best configuration for a given scenario, troubleshooting why scaling is not working, or interpreting log data to determine why an instance was terminated. A common trick question asks you to select the correct metric for scaling. For example, using CPU percentage is common, but if the application is I/O-bound, disk queue length might be better. Another trap involves the cooldown period. If you set cooldown too short, scaling actions can thrash, adding and removing instances too rapidly. The exam expects you to understand the impact of these configuration parameters.

To prepare, you should practice creating a scale set in a free Azure account. Run a load test using a tool like Apache JMeter and watch the scale set add and remove instances. Also, study the autoscale best practices documentation from Microsoft, as exam questions are directly drawn from that material. Knowing that scale sets support both scale out and scale in, and that you can configure a minimum and maximum number of instances, is the bare minimum. The deeper understanding comes from knowing how to integrate with Application Insights for custom metrics, and how to use instance protection to prevent critical instances from being terminated during scale-in events.

## How it appears in exam questions

Exam questions on VM scale sets typically present a scenario where an application must handle variable traffic with minimal manual intervention. A common pattern is: A company runs a stateless web application on Azure. Traffic spikes unexpectedly during holidays. They want to automatically add VMs when CPU usage exceeds 80% for five minutes and remove VMs when it drops below 30% for ten minutes. What should they configure? The answer is a VM scale set with autoscale rules. The question might then ask for the exact metric, the duration, or the action.

Another frequent question type is about scaling policies. For example, You have a scale set with 5 instances. CPU usage is at 90% for 15 minutes. The scale-out rule adds 2 instances with a cooldown of 5 minutes. After 5 minutes, the CPU is still at 88%. How many instances will be running? This tests your understanding of cooldown periods and that the rule triggers only after the duration threshold is met. The answer in this case would be 7, because the first scaling action completed and the cooldown has expired, so a new scaling action can start.

Questions also appear about the difference between vertical scaling and horizontal scaling. A question might say A database server is running out of memory. Should you add a VM to the scale set? The answer is no, because scaling out adds more servers, not more resources to an existing server. That is vertical scaling. VM scale sets are for horizontal scaling. This distinction is frequently tested.

Troubleshooting questions are also common. A learner might see A scale set is configured to scale out when CPU > 70%. It has a minimum of 2 and a maximum of 10 instances. The CPU has been at 80% for 30 minutes, but no new instances are created. What is the most likely cause? Possible answers could be that the scale set has already reached the maximum instance count, that the autoscale rule is disabled, or that the VM configuration has an error. The correct answer depends on the details, but the exam expects you to systematically check the most common issues first.

A more advanced question might involve a scale set that scales in too aggressively, causing instances to be terminated before they finish processing requests. This tests your understanding of instance protection and the scale-in policy. The answer might be to enable instance protection for instances that are processing a batch job, or to adjust the cooldown period to allow longer time before scaling in.

Finally, questions about cost optimization appear. A company has a scale set that runs 24/7, but traffic is very low at night. The exam asks for a cost-saving recommendation. The answer is to configure a scheduled autoscale rule to reduce the instance count during off-hours, or to use a scale set with spot instances for non-critical workloads.

## Example scenario

A startup called QuickShip runs a food delivery website. During lunchtime, from 11 AM to 2 PM, the number of orders spikes as people order lunch. In the afternoon, orders drop. They want to make sure the website stays fast during lunch but do not want to pay for idle servers in the afternoon.

They decide to use a VM scale set. They create a scale set with two VMs as the minimum and ten VMs as the maximum. They configure an autoscale rule: If the average CPU usage across all VMs goes above 75 percent for ten minutes, add two more VMs. They also set a rule to remove two VMs if CPU drops below 30 percent for fifteen minutes.

At 10:45 AM, the website has two VMs running, and the CPU is at 20 percent. As 11 AM approaches, people start browsing and ordering. CPU climbs. By 11:10 AM, CPU reaches 75 percent. The autoscale system waits ten minutes to confirm the trend. At 11:20 AM, CPU is still above 75 percent, so the scale set adds two new VMs. Now there are four VMs. The load is distributed, and CPU drops to 50 percent.

But orders keep increasing. By 11:45 AM, CPU hits 75 percent again. The scale set adds two more VMs, bringing the total to six. This continues until the peak at 12:30 PM, when the scale set has eight VMs. CPU stays around 70 percent, which is acceptable.

Around 1:30 PM, orders begin to slow. CPU drops below 30 percent at 2 PM. After fifteen minutes, it is still low, so the scale set removes two VMs. This process repeats during the afternoon. By 4 PM, the scale set is back to the minimum of two VMs.

QuickShip only pays for the VM hours used. During lunch, they used more VMs and paid more. In the afternoon, they used fewer VMs and paid less. They never had to log in to manually add or remove servers. The website stayed fast, and the cost was reasonable.

If a VM in the scale set fails, the scale set automatically replaces it with a new one from the same configuration template. This keeps the website available even if hardware fails. The startup does not need to worry about server maintenance or capacity planning.

## Common mistakes

- **Mistake:** Thinking a VM scale set can scale a single VM up or down (vertical scaling).
  - Why it is wrong: VM scale sets only add or remove entire VMs (horizontal scaling). They do not change the size of an existing VM, like increasing its RAM or CPU. Vertical scaling requires stopping the VM and changing its SKU, which is a different process.
  - Fix: Understand that scale out means more instances, scale up means bigger instances. For dynamic workload changes, use a scale set. For permanently underpowered single VMs, consider resizing the VM.
- **Mistake:** Setting cooldown periods too short, causing thrashing.
  - Why it is wrong: If the cooldown is too short, the scale set may react to temporary spikes and add VMs, only to remove them moments later when the spike passes. This wastes resources and can trigger costs unnecessarily. It also destabilizes the application.
  - Fix: Set cooldown periods of at least 5 to 10 minutes for scale-out and 10 to 15 minutes for scale-in. This gives the new instances time to become fully operational and the metrics time to stabilize.
- **Mistake:** Scaling based on CPU for an application that is I/O-bound.
  - Why it is wrong: If the bottleneck is disk I/O or network bandwidth, CPU can be low even under heavy load. Scaling based on CPU will not trigger, and the application will remain slow. The scale set will stay at the minimum instance count.
  - Fix: Identify the correct metric that reflects application demand. For I/O-bound apps, use disk queue length or IOPS. For memory-intensive apps, use memory percentage. Custom metrics from Application Insights can also be used.
- **Mistake:** Assuming all VMs in a scale set are updated automatically when the template changes.
  - Why it is wrong: Changing the VM configuration template does not automatically update running instances. The scale set uses an upgrade policy. If the policy is set to Manual, you must manually upgrade each instance. If set to Automatic, the scale set will upgrade all instances, but you must initiate the upgrade operation.
  - Fix: Understand the three upgrade policies: Automatic, Rolling, and Manual. After updating the model, you need to trigger an upgrade for the instances to reflect the changes.
- **Mistake:** Deploying stateful applications on VM scale sets without instance protection.
  - Why it is wrong: VM scale sets are designed for stateless applications. If an instance holds unique data (like session state in memory) and the scale set removes it during scale-in, that data is lost. Customers may be logged out or lose work.
  - Fix: For stateful workloads, store session state in a shared database or cache like Azure Redis Cache. Alternatively, enable instance protection on critical instances to prevent them from being terminated during scale-in.

## Exam trap

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## Commonly confused with

- **VM scale set vs Virtual Machine availability set:** An availability set is a logical grouping of VMs that protects against hardware failures within a data center by distributing VMs across fault domains and update domains. It does not provide autoscaling. A VM scale set, on the other hand, provides both high availability through fault domains AND automatic scaling. You use an availability set for a fixed number of VMs that need high availability but do not need to scale in and out. (Example: If you have a pair of domain controllers that must stay up, place them in an availability set. If you have a web server farm that grows and shrinks, use a VM scale set.)
- **VM scale set vs Azure App Service Plan scaling:** Azure App Service is a platform-as-a-service that can also scale out by adding more instances. However, App Service runs on a managed platform, so you do not manage the underlying VMs. VM scale sets give you full control over the VM configuration, OS updates, and extensions. You choose App Service when you want less management overhead. You choose VM scale sets when you need full control or run custom software that cannot run in App Service. (Example: If you want to host a PHP web app with specific PHP extensions that App Service does not support, use a VM scale set. If your app is a standard .NET or Node.js app, App Service is simpler.)
- **VM scale set vs Azure Instance Groups (Google Cloud) or Auto Scaling Groups (AWS):** These are the equivalent services in other cloud providers. They all provide a group of identical VMs with autoscaling. The core concepts are the same: launch template, scaling policies, load balancer integration. The differences are in naming, configuration APIs, and specific features like Google's preemptible VMs or AWS's mixed instances policies. (Example: A job candidate who knows Azure VM scale sets can easily learn AWS Auto Scaling Groups, because the idea of a launch template and scaling rules is identical.)

## Step-by-step breakdown

1. **Define the VM Configuration Template** — Before creating a scale set, you must define what each VM will look like. This includes the VM size (e.g., Standard_D2s_v3), the operating system image (Windows Server 2022 or Ubuntu 22.04), disk type (SSD or HDD), network settings (virtual network and subnet), and any extensions (like the Azure Monitor agent). This template ensures every instance is identical. Changing the template does not affect running instances until you trigger an upgrade.
2. **Set the Instance Count Limits** — You configure the minimum, maximum, and default instance count for the scale set. The minimum ensures that even during low demand, you always have some capacity to handle requests. The maximum prevents runaway costs if a scaling rule goes wild. The default is the starting number of instances when the scale set is first created. These limits are crucial for cost management and availability.
3. **Configure Autoscale Rules** — This is the heart of the VM scale set. You define one or more rules that specify a metric to monitor, a condition (greater than or less than a threshold), a duration (how long the condition must be true), and an action (increase or decrease instance count by a specific number or percentage). You can have separate rules for scale-out and scale-in. You can also schedule scaling actions for predictable events like peak hours.
4. **Integrate with a Load Balancer** — A scale set alone does not distribute traffic. You must attach it to an Azure Load Balancer or Application Gateway. The load balancer receives incoming traffic and distributes it across all healthy VMs in the scale set. When new VMs are added, they are automatically registered as backends. When VMs are removed, they are deregistered. This integration is critical for the application to function correctly under scale.
5. **Deploy the Scale Set and Monitor** — After configuration, you deploy the scale set. The initial VMs are created from the template. Once running, you should monitor the autoscale activity using Azure Monitor metrics and logs. Check whether scale-out and scale-in events are triggering correctly. Adjust rules if needed. Also, test the behavior by generating load and observing the response. Continuous monitoring ensures the scaling rules remain effective as application usage patterns change.

## Practical mini-lesson

When working with VM scale sets in a real production environment, there are several important considerations that go beyond what exam questions cover. First, you must choose the right orchestration mode. Azure VM scale sets support two orchestration modes: Uniform and Flexible. Uniform mode creates identical VMs from a single configuration model. Flexible mode allows mixing VMs with different sizes or configurations within the same scale set, and it provides better compatibility with modern application patterns, such as running containerized workloads with Kubernetes. For most stateless web apps, Uniform mode is simpler. For advanced scenarios requiring VM diversity, Flexible mode is better.

Second, think about what happens when a VM is removed during scale-in. By default, the scale set uses the NewestVM policy, which removes the most recently created instances first. Alternatively, you can choose OldestVM, which removes the ones that have been running the longest. For long-running tasks, you might want to use the Default policy, which balances between fault domains. More importantly, if instances are in the middle of processing requests, abrupt termination can cause data loss or incomplete transactions. In practice, you should design your application to handle graceful shutdown. This often involves listening to the Azure Metadata Service signal that indicates an instance is about to be terminated, then completing any in-flight work.

Third, understand that scaling is not instantaneous. Creating a new VM from scratch can take several minutes, depending on the OS image size and extensions. If you need faster response to sudden spikes, you can use a scale set with a custom image that is pre-configured and located in a Shared Image Gallery in the same region. This can reduce provisioning time to under a minute. Alternatively, you can use a scale set with a startup script that runs quickly, but even then, the VM must boot and the application must become ready.

Fourth, be aware of cost considerations. Scaling out adds cost, but scaling in reduces it. However, you are always billed per second for running VMs. If you set your scale-up threshold too aggressively, you might add instances that are barely used, wasting money. Conversely, if you set it too conservatively, users experience slow performance. The best approach is to monitor application performance metrics, such as request latency, and use that as a custom metric for scaling, rather than relying solely on CPU.

Finally, do not forget about network security groups (NSGs). Each VM in a scale set can have an NSG applied at the subnet or NIC level. If you plan to scale out to many instances, ensure the NSG rules do not become a bottleneck. Also, consider using Azure Bastion for secure access to VMs, since you cannot easily RDP or SSH into hundreds of instances individually. Instead, you should use configuration management tools like Ansible or PowerShell Desired State Configuration to manage the instances at scale.

a VM scale set is a powerful tool, but it requires careful planning around orchestration mode, scaling thresholds, provisioning time, cost, and operational access. The most successful implementations treat the scale set as part of a larger infrastructure-as-code deployment, using ARM templates or Terraform to version and automate everything.

## Memory tip

Think of a VM scale set as an elastic band: it stretches out when demand pulls harder, and snaps back when the pull is gone.

## FAQ

**Can I use a VM scale set for a single VM?**

Yes, you can set the minimum and maximum to 1, which effectively gives you a single VM. However, you would lose the autoscaling benefit. For a single VM, it is simpler to use a regular virtual machine.

**How do I update the software on all VMs in a scale set?**

You update the VM configuration template, then perform an upgrade. You can do a rolling upgrade to update instances in batches, minimizing downtime. For frequent updates, consider using a custom image and building a new version, then updating the scale set to use the new image.

**What happens if I manually stop one of the VMs in the scale set?**

The scale set will detect that the instance is unavailable and may try to replace it, depending on the repair policy. To prevent this, you can use instance protection. Otherwise, it is better to use the scale set's deallocate action rather than manual stop from the portal.

**Can I attach a different load balancer to the scale set after creation?**

Yes, you can update the scale set to use a different load balancer, but you must reconfigure the network profile. This can be done via the Azure portal or CLI, but it may cause temporary disruption.

**Does a VM scale set support spot instances?**

Yes, Azure supports using Spot VMs in a scale set, which are unused capacity at a lower price but can be evicted when Azure needs the resources. This is cost-effective for batch jobs and fault-tolerant workloads.

**How do I monitor the scaling events?**

You can view scaling events in the Azure Monitor activity log, which shows when scale set operations start and complete. You can also set up alerts for failed scaling actions or unusual metric patterns.

**Can I use a VM scale set with an existing virtual network?**

Yes, when you create a scale set, you specify a virtual network and subnet. The scale set's VMs will be created in that subnet. You can use an existing VNet that has the appropriate address space.

## Summary

A VM scale set is a fundamental Azure resource for building scalable, resilient, and cost-efficient cloud applications. It automates the process of adding and removing identical virtual machines based on real-time demand, which is a core principle of cloud elasticity. By using a scale set, you ensure that your application can handle traffic spikes without manual intervention, and you avoid paying for idle resources during low-demand periods.

From an exam perspective, VM scale sets appear in Azure Administrator, Azure Solutions Architect, and other cloud certification exams. You need to understand the configuration of autoscale rules, the difference between scale out and scale up, the role of load balancers, and the implications of upgrade policies. Common mistakes include confusing scale sets with availability sets, setting cooldown periods too short, and using wrong metrics for scaling.

The practical takeaway is that VM scale sets are not just an exam topic but a real-world tool used by thousands of organizations. Whether you are hosting a simple website or a complex microservices architecture, scale sets provide the automation needed to maintain performance without over-provisioning. By mastering VM scale sets, you demonstrate a solid understanding of cloud infrastructure and the ability to design systems that are both efficient and resilient. As you continue your IT certification journey, treat VM scale sets as a building block for mastering more advanced concepts like autoscaling, load balancing, and infrastructure as code.

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Practice questions and the full interactive page: https://courseiva.com/glossary/vm-scale-set