Pass Your Microsoft Azure AZ-204 Exam Easy!

Microsoft AZ-204 Developing Solutions for Microsoft Azure exam dumps vce, practice test questions, study guide & video training course to study and pass quickly and easily. Microsoft AZ-204 Developing Solutions for Microsoft Azure exam dumps & practice test questions and answers. You need avanset vce exam simulator in order to study the Microsoft Azure AZ-204 certification exam dumps & Microsoft Azure AZ-204 practice test questions in vce format.

Develop Azure Infrastructure as a Service compute solutions

17. AZ-203 - Lab - Azure Batch - .Net SDK - v9.0

Hi and welcome back. Now in this chapter, let's discuss using the latest version of the Azure Batch to get packages. So, if I go on to get the solution this time for Microsoft Azure Batch, I have version 12.0 over here. So as I mentioned before, let's also see how to use the new version. What's the key difference? So there's just a slight difference when you actually look at how you basically reference resource files. So a lot of the code itself remains, saying there is no change in the creation of the pool of virtual machines. This remains the same if it comes to the creation of Job—no changes whatsoever. Now there is one change, which is basically the reference to the resource file. So over here now, when I make a reference to the resource file, I have to mention that I am actually taking it from a storage container URL. So this is how you actually create your resource file. You would eventually add to a list of resource files, but there is one important change that you must now make when taking the files from a storage container. Now, apart from that, in this programme I just kind of want to showcase the use of output in storage containers. So what we've seen so far is that the AzureBatch account uses the Ffmpg to take an input video file from our storage account, and currently the output is being stored on the virtual machine itself. But now let's say that I want to store the output. So basically that audio file is in an output container in a storage account, so we can do that as well. So if I go on to a storage account, if I go on to the block service, over here I have an output storage container. So I want the output of the batch job, or the task, to go into this storage container. So what are the changes I'm going to be making in the code? Well, the first thing is to basically ensure that again we generate a shared access signature, this time for the output container as well. Now, in terms of the shared access signature for the input container, please note that since we're only reading from our container, our sampleMP4 file over here, the permission I'm giving is just the read and list permissions. Whereas I'm currently creating two shared access signatures, I'm turning off permission for the output container. Right over here, I'm specifying the incontainer name and the output container name. So if I go on top over here, I'm mentioning the container names if I scroll back down. So I'm going ahead and creating both an SASS token and a container URL for both my input and output container, and if I scroll all the way down now, what I'm also doing is that in addition to adding the resource files, I'm also telling my input files where to copy or where to go. I'm also mentioning an output file list. So here I'm mentioning my output container and, as part of my task output files, I'm actually mentioning a reference to that container. So this is another key difference I've just added on top of this program. This is also important from an exam perspective. Now, before we actually go ahead and run our program, I just want to go onto the output container in our storage account. So here, I don't have any files or any blobs in this container itself. Now, if I go on to the program, what I'm going to do is that I'm going to execute the methods to create a job and do the task, because I already have a pool in place. So let me go ahead and run this program. So it's gone ahead, created the job, and created the task. Now, if I go on to the task, I can see that it is completed. Let me go on to the output container. Let me click on refresh, and now you can see the audio file. So now, automatically, as part of the task itself, we are mentioning that whatever output is created as part of the task is sent on to this container in our storage account, right? So this marks the end of this lab.

18. AZ-203 - Lab - Azure Batch - .Net SDK - v12.0

Hi, welcome back. Now, before we move on to understanding the workings of Azure Kubernetes and the Azure container, I thought let's have a primer on understanding containers and Docker. So this is for those students who are not well aware of the concept of containers or Docker. So maybe you've heard of the concept of containers and dockers but don't know that much in detail. So I just thought, let's have a few chapters to explain containers and Docker. Before we get into the specifics of Docker, it's important to understand the evolution of application deployment. So in the past, let's say a company had three applications that they wanted to deploy. They have or provision one physical server. So remember, this is before the evolution of virtual machines. So let's say that they deploy all of the applications on this physical server. Now, these applications could also have dependencies. So let's say that two applications have the same set of dependencies. So this could be modules, this could be libraries pertinent to the OS itself, and maybe you have another application with another set of dependencies. Now, on this physical server, you have all three applications running. Now, let's say that you make a change to this application. Now, when you're making a change to this application, let's say that as part of the change, you are making a change to one of its dependencies. Now, since this application has the same set of dependencies, let's say that the change you have made to this particular application has now caused an issue with this application. So this was a very common scenario. Making changes to an application on a machine that has a set of applications was always an issue because you didn't know what effect making a change to one application would have on the other applications running on this physical server. So this was always an issue. We then came to the evolution of having something known as "virtual machines." So on one physical box, you could create isolated environments. So many different virtual machines here. Now, what is the benefit of this? It means that each virtual machine could have a different operating system. So on the underlying physical server, you could install hypervisor-based tools. So maybe you could install a tool known as VMware. And on this, you could host a set of virtual machines. These virtual machines would do a good job of utilising the underlying resources of the virtual and physical services. So it will make good use of the available amount of CPU, the available amount of RAM, and the amount of external storage or internal storage of the physical server. You could then deploy each application separately on each virtual machine. So even if you had to make a change in one application, it would not break the other applications running on the same physical box because each of them would be running in a separate virtual machine. So the same dependency would be installed on this virtual machine. It would be installed on this virtual machine, and then you have this dependency. So this was the next evolution, wherein you had virtual machines that could distribute your application or isolate your application deployment. And now we have the next generation of application deployment, which is basically your container deployment. So again, you have your physical box, the physical box. Again, you install your hypervisor and your virtual set of tools. On this, again, you can host your virtual machines. Now, on the virtual machine itself, you could deploy your application within or in a container. So your application will be embedded inside a container, and this container could run on a virtual machine. Now, what were the core benefits of this? Well, first of all, the container is basically a package of your OS, your operating system, maybe any system libraries that you require for your application to run, and then your app itself. Now, you could run many containers on a virtual machine. So instead of actually having separate virtual machines for your application, you could have your application packaged as containers. And all of these containers could run on one virtual machine. So instead of having one application running on separate virtual machines, you just run the containers on a single virtual machine. If you look at the size of the container itself, you might think, "What's the difference between running the application as a container on a virtual machine?" because the container itself contains the OS. So what is the container size? Be big? No, even if the virtual machine's operating system, say, Officer, is installed on it. If you look at the size of the OS on a container, one gigabyte could be just a handful of megabytes. So the OS, which is part of your container, is just kind of a lightweight OS. It's just like the bare essentials of an operating system, which are required to run your application. So when you compare the size of a container with the size of the OS, it's much less. Apart from that, we have the advantage of running multiple containers on a virtual machine. And the other benefit of containerizing your applications is that you can actually move these containers onto different virtual machines. Now, how can you accomplish this? How do containers run on a virtual machine when you have to install something else on the virtual machine? and that's known as a container tool set. Now, the most popular container toolset is Docker. So you have to install the Docker engine on the virtual machines, and the Docker engine is then responsible for running your containers. Now, in the subsequent chapters, we are going to look at how to work with Docker and how to work with containers, and then we'll move on to the orchestration of containers using Azure Kubernetes. For now, this marks the end of this chapter where we'll give an introduction to containers and Docker.

19. AZ-203/204 - Primer on Docker Containers

So let's have a continuation of what's Docker. So, this is basically a toolset that can be used to create, deploy, and run your applications. Now, this tool set can be used to create, deploy, and run your applications in containers. So, what exactly is a container? Well, this is a standard unit of software. This contains software code and whatever is required to run your software code. It is also lightweight and standalone in nature. You can easily put this container in any computing environment and run it there. So, this is the definition of a container. and then we come. Why do we want to use Docker? Well, Docker helps to containerize your application. So this is a tool set. It makes it much easier to maintain and deploy your application. By isolating the application into a container, it becomes much easier for the IT team to maintain and deploy your application. The Docker container is much smaller in size than if you had your application deployed on a virtual machine. So because of its smaller size, it becomes easier and faster to deploy. It also helps to reduce the size of and cost of your infrastructure. Instead of having separate virtual machines for your application's deployment, deploying it in terms of containers helps reduce the amount of virtual machines and infrastructure that you require for your applications. You can also build common Docker images. So, let's say that you have common libraries or common modules that are shared between applications. You can actually create these Docker images and then include them in your application image. Since you can deploy the same application on different computing environments and it works this way, it reduces the main blame game, which is why it does not work on my computer. So, normally, developers will have their application working on the system, but when they deploy that application onto, let's say, a test environment or a staging environment, it does not work. But they keep on saying that it works on my computer. Why doesn't it work in the test or staging environment? Docker will ensure that the same application and container work in different computing environments by deploying it as application containers. So these are some of the most important aspects of why you should use Docker. Right? So at the end of this chapter, in a subsequent lab, we'll see how to make use of the Docker tool set.

20. AZ-203/204 - Continuation on What is Docker

Hi, welcome back. Now, in this chapter, I just want to go through the installation of the Docker engine and how you can run a simple container on a virtual machine using the Docker-based tools. So this is just to give a student a better understanding when it comes to the installation of Docker on a virtual machine. So first, let me go ahead and add a new resource. I'm going to go ahead and add a Linux virtual machine. So I'll choose Ubuntu Server. I'll choose my resource group. I'll go ahead and give it a virtual machine name. I'll choose the location. I'll leave everything as it is. Let me enter the account details in the selected ports. I'll leave it at Port 22. Let me also go ahead and select Port 80. Go on to Next for the disk; leave everything as it is. I'll make it part of an existing network. If you want, you can also go ahead and create a new virtual network. Let me proceed to Next for management. I'll just turn off boot diagnostics. Go on to Next for the advanced let mego on to review and create and let mego ahead and create this virtual machine. Now, once I have the virtual machine in place, I'll go onto the resource. So I'm going to go ahead and actually connect to this virtual machine. So I'll use PuTTY to go ahead and connect to the virtual machine. Now, once I'm connected to the virtual machine, I'm going to go ahead and execute some commands to install Docker on this underlying virtual machine. So first I'm going to go ahead and update the package index. Once this is done, I'll install packages to ensure that this machine can install the repository packages over HTTPS. Next, I'll go ahead and issue the command to add the Docker official key. Next, I'll go ahead and set up a stable repository for Docker. Next, I'll again go ahead and update the package index, and then I'll go ahead and install Docker. So this is the list of steps you can actually perform on your virtual machine to go ahead and install Docker. Now you should always go on to the Docker documentation and see the installation steps, the installation steps for different operating systems, and the different versions of Docker. So this keeps on getting updated. So if you want to install Docker on a virtual machine, please go ahead and use the Docker documentation. Now, once you have Docker installed, you can now go ahead and download images from a place known as, or possibly known as, Docker Hub. And those images could be run as containers on this virtual machine. So the Docker engine on this virtual machine can go ahead, take the image, and then run it as a container. So if I go onto the site HubDocker.com, here is where you can see the reports for all of the images that are available. So if I go and, let's say, search for the NGINX image. So let's say I want to run the NGINX web server on Docker. So over here, you can go ahead and go to the container that's available, and you can see all the information on how you can go ahead and run the container and work with the container on Docker. So I'm going to go ahead. Let me clear the screen. I'm going to execute the command to pull a very specific version of NGINX onto this particular system. So now what this is doing is that it's going to the Docker engine and going ahead and downloading the image from Docker Hub. So by default, the Docker engine understands that if a command is given to go ahead and pull a specific version of an image, it will actually go on to Docker Hub and pull that image. So over here, you can see that you now have the image in place. If you want to see the images on the system, engine X 1.170, you can run the following command line images: So over here, you can see that you have one image in place. So let's see in the next image: Now, if I want to go ahead and run the container, I can go ahead and execute the following command. So over here I'm specifying the name of the image, which is Internet One 70. I'm giving a name to my container. I'm using the pseudo-docker command. And over here I'm saying to map the internal port of the container onto the port of the virtual machine. So let me go ahead and run this. So now I should be having NGINX running in a container on this virtual machine. So please note, I have not gone ahead and installed a web server on the Soviet machine. I've installed Docker. And then, with the help of Docker, I'm now running an NGINX container. Now, if I go on to the Linux VM, if I take the public IP address, let me go on to a new tab so I can see the home page for NGINX. So now remember: this web server is running in a container on the Linux virtual machine. At any point in time, you can go ahead and stop the container, and that would stop the Web server from running, right? So this marks the end of this chapter. I just want to give an idea of how Docker works. So we've gone ahead and installed the Docker engine, and then we ran a container. We basically pulled an image from Docker Hub, and then we could run a container out of that image.

21. AZ-203/204 - Lab - Look at Docker

Hi and welcome back. So in one of our earlier chapters, we saw how we could deploy Docker on a Linux virtual machine. We had also seen a command that was used to go ahead and run an index web server in a container. So let's have a review of these steps. In the following chapter, we'll see how to first deploy a Net Core application on a Linux virtual machine and then Dockerise that application. So on our Linux VM virtual machine, we first went ahead and installed Docker, followed by running this Docker command. So we want to run a particular version of NGINX. So this is known as the tag for the NGINX image. So remember, that image contains all the information that is required, or basically the template for running NGINX as a web server. So when we ran this command, it first had to go out and get the image. So this image is available through Docker Hub. So the underlying Docker engine understands that in order to run a container, it needs an image. and if that image is not present, it will go on Docker Hub. It will go and pull that image onto the local virtual machine, and then run it as a container. You give a name for the container, and you do a port mapping. This port mapping allows the container to expose its internal port to the Linux VM water machine. So remember, you can also perform the same list of steps on a Windows VM water machine. We have to ensure that the right image is used, which basically works on a Windows VM virtual machine. So this is just a quick review of what happened. I said in the subsequent chapters we were going to see how to authorise a net-based application.

Go to testing centre with ease on our mind when you use Microsoft Azure AZ-204 vce exam dumps, practice test questions and answers. Microsoft AZ-204 Developing Solutions for Microsoft Azure certification practice test questions and answers, study guide, exam dumps and video training course in vce format to help you study with ease. Prepare with confidence and study using Microsoft Azure AZ-204 exam dumps & practice test questions and answers vce from ExamCollection.