Microsoft AZ-400 Practice Test - Questions Answers, Page 5

The process to associate an automated test with a test case is:

1. Create a test project containing your automated test. What types of tests are supported?

2. Check your test project into an Azure DevOps or Team Foundation Server (TFS) repository.

3. Create a build pipeline for your project, ensuring that it contains the automated test. What are the differences if I am still using a XAML build?

4. Use Visual Studio Enterprise or Professional 2017 or a later version to associate the automated test with a test case as shown below. The test case must have been added to a test plan that uses the build you just defined.

Reference:

https://docs.microsoft.com/en-us/azure/devops/test/associate-automated-test-with-test-case

You can set up a CI/CD pipeline to deploy your apps on a Kubernetes cluster with Azure DevOps by leveraging a Linux agent, Docker, and Helm.

Step 1: Create a service principle in Azure Active Directory (Azure AD)

We need to assign 3 specific service principals with specific Azure Roles that need to interact with our ACR and our AKS. Create a specific Service Principal for our Azure DevOps pipelines to be able to push and pull images and charts of our ACR. Create a specific Service Principal for our Azure DevOps pipelines to be able to deploy our application in our AKS. Step 2: Add a Helm package and deploy a task to the deployment pipeline

This is the DevOps workflow with containers:

Step 3: Add a Docker Compose task to the deployment pipeline.

Dockerfile file is a script leveraged by Docker, composed of various commands (instructions) and arguments listed successively to automatically perform actions on a base image in order to create a new Docker image by packaging the app.

Reference:

https://cloudblogs.microsoft.com/opensource/2018/11/27/tutorial-azure-devops-setup-cicd-pipeline-kubernetes-docker-helm/

Step1: Create an Azure Automation account.

An Azure Automation account is required.

Step 2: Install the custom Desired State Configuration (DSC) extension on the virtual machines

Under the hood, and without an administrator having to remote into a VM, the Azure VM Desired State Configuration extension registers the VM with Azure Automation State Configuration.

Step 3: Onboard the virtual machines to the Azure Automation account.

Step 4: Complete the Desired State Configuration (DSC) configuration.

Create a DSC configuration.

Reference:

https://docs.microsoft.com/en-us/azure/automation/automation-dsc-onboarding

Box 1: "Microsoft.Resources/deployments"

Reference a secret with dynamic ID. You need to reference a key vault secret that varies based on the current deployment.

Example:

"resources": [

{

"apiVersion": "2018-05-01",

"name": "dynamicSecret",

"type": "Microsoft.Resources/deployments",

"properties": {

"mode": "Incremental",

"templateLink": {

Box 2: "templateLink"

In your parent template, you add the linked template and pass in a parameter that contains the dynamically generated resource ID.

References: https://docs.microsoft.com/en-us/azure/azure-resource-manager/resource-manager-keyvault-parameter

Box 1: rwx

The Log Analytics agent for Linux runs as the omsagent user. To grant >write permission to the omsagent user, run the command setfacl -m u:omsagent:rwx /tmp.

Box 2: /tmp

Deploying DSC to a Linux node uses the /tmp folder.

Reference:

https://docs.microsoft.com/en-us/azure/automation/automation-dsc-onboarding

Box 1: Get-AzResource

Use the following command to examine the access control mode for all workspaces in the subscription:

PowerShell

Get-AzResource -ResourceType Microsoft.OperationalInsights/workspaces -ExpandProperties | foreach {$_.Name + ": " + $_.Properties.features.enableLogAccessUsingOnlyResourcePermissions

Box 2: -ResourceType

Reference:

https://docs.microsoft.com/en-us/azure/azure-monitor/logs/manage-access

Batching CI runs

If you have many team members uploading changes often, you may want to reduce the number of runs you start. If you set batch to true, when a pipeline is running, the system waits until the run is completed, then starts another run with all changes that have not yet been built.

Example:

# specific branch build with batching

trigger:

batch: true

branches:

include:

- master

To clarify this example, let us say that a push A to master caused the above pipeline to run. While that pipeline is running, additional pushes B and C occur into the repository. These updates do not start new independent runs immediately. But after the first run is completed, all pushes until that point of time are batched together and a new run is started.

Reference:

https://docs.microsoft.com/en-us/azure/devops/pipelines/repos/github