Pass the Microsoft Azure AZ-400 Questions and answers with Dumpstech

Task 9: Initialize the main branch (if it doesn’t exist) and create a pull request-triggered pipeline for .NET Core build

Step 1: Initialize the main Branch

In your Azure DevOps Project (Project1), navigate to Repos .

In the left menu, click Files .

If you see a message like “This repository is empty,” click Initialize .

Create a README.md file (or another file) and set the default branch name to main.

Click Initialize .

If the main branch already exists, you can skip this step.

Step 2: Create a New Branch for the Pipeline

In Repos > Files , click on the Branches tab.

Click New branch .

Enter:

Branch name: azure-pipelinesT

Based on: main

Click Create .

This creates a new branch named azure-pipelinesT from main.

Step 3: Create the Pipeline YAML File in the New Branch

Switch to the azure-pipelinesT branch.

In Files , click New file .

Name the file: azure-pipelines.yml.

Step 4: Write the YAML Pipeline for .NET Core Build

Here’s a sample YAML file:

trigger: none

pr:

branches:

include:

- main

pool:

vmImage: ' windows-latest '

variables:

buildConfiguration: ' Release '

steps:

- task: UseDotNet@2

displayName: ' Install .NET Core SDK '

inputs:

packageType: ' sdk '

version: ' 8.x ' # Or specify the exact version of .NET Core SDK you need

installationPath: $(Agent.ToolsDirectory)/dotnet

- script: dotnet build src --configuration $(buildConfiguration)

displayName: ' Build .NET Core project '

This pipeline:

Runs only when a pull request is created or updated targeting the main branch.

Uses the windows-latest agent.

Installs the .NET SDK and builds the project in the src directory.

Step 5: Commit the Pipeline File

In the bottom commit message box:

Message: Add PR-triggered pipeline for .NET Core build

Ensure the branch is set to azure-pipelinesT .

Click Commit .

Step 6: Create the Pipeline in Azure DevOps

In the left menu, click Pipelines .

Click New pipeline .

Select:

Azure Repos Git .

Select your repository.

Choose Existing Azure Pipelines YAML file .

In the branch selector, choose the azure-pipelinesT branch.

Select the YAML file path (azure-pipelines.yml).

Click Continue and Run to validate.

Step 7: Test the Pipeline Trigger

Create a new branch (e.g., feature/test-pr).

Push a commit and create a pull request targeting main.

Verify that the pipeline runs automatically for this PR in Pipelines .

Task 7: Create a Pipeline to Deploy a Docker Image in a New Branch

Step 1: Create a New Branch

In your Azure DevOps Project (Project1), navigate to Repos .

In the left menu, click on Branches .

Click New branch .

Enter:

Branch name: azure-pipelines

Based on: main (or your default branch)

Click Create .

This creates a new branch named azure-pipelines.

Step 2: Switch to the New Branch

In Repos , click on Files .

In the top-left branch selector, choose azure-pipelines .

Step 3: Use the Predefined Docker Pipeline Template

Azure Pipelines has predefined YAML templates for common tasks, including Docker.

Here’s how to add it:

In the azure-pipelines branch, click New file .

Name the file: azure-pipelines.yml.

Click the Show templates button in the YAML editor (if available).

Search for the Docker template in the list of predefined templates.

Select the Docker template to auto-populate the YAML file.

If the editor doesn’t show the template picker, you can manually add the following basic Docker pipeline template:

yaml

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# Predefined Docker pipeline template

trigger:

- main

resources:

- repo: self

variables:

imageName: ' mydockerimage '

stages:

- stage: Build

displayName: Build and push stage

jobs:

- job: Build

pool:

vmImage: ' ubuntu-latest '

steps:

- task: Docker@2

displayName: Build and push an image to container registry

inputs:

command: buildAndPush

repository: $(imageName)

dockerfile: ' **/Dockerfile '

containerRegistry: ' < your-container-registry-service-connection > '

tags: |

latest

Replace < your-container-registry-service-connection > with your actual Azure Container Registry (ACR) or DockerHub service connection name.

Step 4: Commit the Pipeline to the New Branch

In the YAML editor, review and adjust if needed (like setting the correct container registry service connection).

In the bottom commit message box:

Message: e.g., Add Docker deployment pipeline

Ensure the branch is set to azure-pipelines .

Click Commit (not commit to main).

This creates the pipeline YAML file in the new azure-pipelines branch.

Step 5: Create the Pipeline in Azure DevOps

In the left menu, click Pipelines .

Click New pipeline .

Choose:

Azure Repos Git .

Select your repo.

Select Existing Azure Pipelines YAML file .

In the branch selector, choose the azure-pipelines branch.

Select the YAML file path (azure-pipelines.yml).

Click Continue and then Run to validate the pipeline.

To create an instance of Azure Application Insights namedaz400-38443478-mainand configure it to receive telemetry data from an Azure web app with the same name, you’ll need to follow these steps:

Create a Log Analytics Workspace:

Go to theAzure Portal.

Search for Log Analytics Workspaces and select Add.

Select a Subscription and either use an existing Resource Group or create a new one.

Provide a unique name for your Log Analytics workspace.

Choose the Region that is appropriate for you.

Review the settings and then selectCreate1.

Create an Azure Application Insights Instance:

In the Azure Portal, navigate to Application Insights.

Click on + Create.

Fill in the instance details, ensuring the name isaz400-38443478-main.

Link the instance to the Log Analytics workspace you created in the previous step.

Review and create the Application Insights instance2.

Configure the Azure Web App to Send Telemetry Data:

Go to the Azure Web Appaz400-38443478-main.

Under Monitoring, selectApplication Insights.

Choose to use an existing resource and select the Application Insights instance you created.

Follow the prompts to set up the connection, which may involve adding the appropriate SDK to your web app and configuring the connection string or instrumentation key.

Verify Telemetry Data Reception:

After setting up, send some test traffic to your web app.

Then, go to the Application Insights instance and check the Overview or Performance sections to see if telemetry data is being received.

Remember to replace placeholder names with the actual names of your resources where necessary. These steps will help you set up Azure Application Insights to monitor your web app effectively.

Task 11: Create an Artifact Feed with Specific Retention Policies

Step 1: Navigate to Artifacts

In your Azure DevOps Project (Project1), click on the Artifacts section in the left menu.

Step 2: Create a New Feed

Click on the + New feed button.

In the Name field, enter:

nginx

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artifact_feed

Choose the visibility of the feed:

Project scoped (only accessible to Project1).

Or Organization scoped (accessible across your Azure DevOps organization).

Leave other settings at default unless you have specific permissions or upstream sources.

Click Create .

This creates a new feed named artifact_feed.

Step 3: Configure Retention Policies

In the Artifacts section, click on the feed named artifact_feed to open its details page.

In the top-right corner, click on the three dots (...) and select Feed settings .

Step 4: Set the Maximum Number of Versions to Retain

In the Feed settings page, find the Retention policies section.

Set Maximum number of versions per package to:

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10

Click Save .

This ensures that only the latest 10 versions of any package are retained in the feed.

Step 5: Set the Downloaded Package Retention Period

In the Feed settings page, under Retention policies , find Downloaded package retention .

Set Number of days to retain downloaded packages to:

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90

Click Save .

This ensures that downloaded packages will be retained in the cache for 90 days before deletion.

To enable streaming of diagnostic telemetry for a single or a pooled database, follow these steps:

1. Go to Azure SQL database resource.

2. Select Diagnostics settings.

3.Select Turn on diagnostics if no previous settings exist, or select Edit setting to edit a previous setting. You can create up to three parallel connections to stream diagnostic telemetry.

4. Select Add diagnostic setting to configure parallel streaming ofdiagnostics data to multiple resources.

5. Enter a setting name for your own reference.

6. Select a destination resource for the streaming diagnostics data: Archive to storage account, Stream to an event hub, or Send to Log Analytics.

7. For the standard, event-based monitoring experience, select the following check boxes for database diagnostics log telemetry: QueryStoreRuntimeStatistics

8. For an advanced, one-minute-based monitoring experience, select the check box for Basic metrics.

9. SelectSave.

1. Open Microsoft Azure Portal and Log into your Azure account.

2. Select network security group (NSG) named az400-9940427-nsg1

3. Select Settings, Outbound security rules, and click Add

4. Click Advanced

5. Change the following settings:

Destination Port range: 8080

Protocol. TCP

Action: Allow

Note: Bydefault, Azure DevOps Server uses TCP Port 8080.

Step 1: Access Azure DevOps

Open your browser and navigate to https://aex.dev.azure.com .

You will be prompted to sign in. Enter the following credentials:

Username: UserUser1-48901628@cxamUsers.com

Password: aFpJ2j-6M!

Click Next or Sign in to proceed.

Note: If you see any Microsoft security challenge (like phone or email verification), follow the instructions to complete it.

Step 2: Sign Up for Azure DevOps

After signing in, you’ll see a prompt to sign up for Azure DevOps.

Use the default settings provided by Azure DevOps for the sign-up process (region, default name, etc.).

Click Continue or Start free with Azure DevOps.

An Azure DevOps Organization is a container for your projects and resources. Default settings are usually fine for new organizations.

Step 3: Create the Organization

Enter a name for your organization. Azure DevOps will suggest a default name (like yourname or org-name), but you can change it if you prefer.

Verify the region is correct.

Click Continue to create the organization.

Your Azure DevOps organization is now created. It’s the top-level container for all your projects.

Step 4: Create a Private Project Named Project1

In your Azure DevOps portal, find the " New Project " button and click it.

Enter the following details:

Project Name: Project1

Description: (optional)

Visibility: Private

Click Create.

A private project means that only users you add can see or access it. This is important for security and privacy.

Step 5: Enforce Maximum File Size for Repositories

By default, Azure DevOps allows files up to 100 MB to be pushed. To ensure the maximum file size for repositories in Project1 is 2 MB, you can enforce this using a pipeline validation.

Azure DevOps does not provide a direct setting for file size limits. However, you can create a pipeline to validate file size on pull requests and block changes if a file exceeds 2 MB.

Here’s how to do it:

Option 1: Enforce with a pipeline validation for pull requests

In your Project1, create a new pipeline.

Use the following YAML for the pipeline:

yaml

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trigger: none

pr:

branches:

include:

- " * "

jobs:

- job: CheckFileSize

pool:

vmImage: ' ubuntu-latest '

steps:

- script: |

echo " Checking file sizes in the PR... "

git fetch origin +refs/pull/*/merge:refs/remotes/origin/pr/*

large_files=$(git diff --cached --name-only | xargs -I{} du -b {} | awk ' $1 > 2097152 {print $2} ' )

if [ -n " $large_files " ]; then

echo " Error: The following files exceed 2 MB: "

echo " $large_files "

exit 1

fi

displayName: ' Check file sizes in PR '

This pipeline will:

Run on pull requests only.

Check the size of all files in the pull request.

If any file is larger than 2 MB (2,097,152 bytes), it will fail the pipeline and block the PR.

Option 2: Local Git configuration (for each developer)

On each developer’s machine, run this command in their local repository:

bash

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git config --local core.bigFileThreshold 2M

This limits the file threshold for checkout, but does not prevent large files from being pushed. Using the pipeline validation (Option 1) is more secure for team environments.