Microsoft AZ-140 : Configuring and Operating Windows Virtual Desktop on Microsoft Azure Exam Dumps

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Exam Number : AZ-140
Exam Name : Configuring and Operating Windows Virtual Desktop on Microsoft Azure
Vendor Name : Microsoft
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AZ-140 Exam Format | AZ-140 Course Contents | AZ-140 Course Outline | AZ-140 Exam Syllabus | AZ-140 Exam Objectives

Exam Number: AZ-140

Exam Name : Configuring and Operating Windows Virtual Desktop on Microsoft Azure

Exam TOPICS

Exam AZ-140: Configuring and Operating Microsoft Azure Virtual Desktop

Candidates for this exam are administrators with subject matter expertise in planning, delivering, and managing virtual desktop experiences and remote apps, for any device, on Azure.

Responsibilities for this role include deploying virtual desktop experiences and apps to Azure. Professionals in this role deliver applications on Azure Virtual Desktop and optimize them to run in multi-session virtual environments. To deliver these experiences, they work closely with the Azure administrators and architects, along with Microsoft 365 Administrators.

Candidates for this exam should have experience in Azure technologies, including virtualization, networking, identity, storage, backups, resilience, and disaster recovery. They should understand on-premises virtual desktop infrastructure technologies as they relate to migrating to Azure Virtual Desktop. These professionals use the Azure portal and Azure Resource Manager templates to accomplish many tasks. This role may use PowerShell and Azure Command-Line Interface (CLI) for more efficient automation.

NOTE: The bullets that appear below each of the skills measured are intended to illustrate how we are assessing that skill. This list is NOT definitive or exhaustive.

Plan an Azure Virtual Desktop Architecture (10-15%)

Implement an Azure Virtual Desktop Infrastructure (25-30%)

Manage Access and Security (10-15%)

Manage User Environments and Apps (20-25%)

Monitor and Maintain an Azure Virtual Desktop Infrastructure (20-25%)

Plan an Azure Virtual Desktop Architecture (10-15%)

Design the Azure Virtual Desktop architecture

 assess existing physical and virtual desktop environments

 assess network capacity and speed requirements for Azure Virtual Desktop

 recommend an operating system for an Azure Virtual Desktop implementation

 plan and configure name resolution for Active Directory (AD) and Azure Active Directory

Domain Services (Azure AD DS)

 plan a host pools architecture

 recommend resource groups, subscriptions, and management groups

 configure a location for the Azure Virtual Desktop metadata

 calculate and recommend a configuration for performance requirements

 calculate and recommend a configuration for Azure Virtual Machine capacity requirements

Design for user identities and profiles

 select an appropriate licensing model for Azure Virtual Desktop based on requirements

 recommend an appropriate storage solution (including Azure NetApp Files versus Azure Files)

 plan for Azure Virtual Desktop client deployment

 plan for user profiles

 recommend a solution for network connectivity

 plan for Azure AD Connect for user identities

Implement an Azure Virtual Desktop Infrastructure (25-30%)

Implement and manage networking for Azure Virtual Desktop

 implement Azure virtual network connectivity

 manage connectivity to the internet and on-premises networks

 implement and manage network security

 manage Azure Virtual Desktop session hosts by using Azure Bastion

 monitor and troubleshoot network connectivity

Implement and manage storage for Azure Virtual Desktop

 configure storage for FSLogix components

 configure storage accounts

 configure disks

 create file shares

Create and configure host pools and session hosts

 create a host pool by using the Azure portal

 automate creation of Azure Virtual Desktop host and host pools by using PowerShell, Command-Line Interface (CLI), and Azure Resource Manager templates

 create a host pool based on Windows client or Windows Server session hosts

 configure host pool settings

 manage licensing for session hosts that run Windows client or Windows Server

 assign users to host pools

 apply OS and application updates to a running Azure Virtual Desktop host

 apply security and compliance settings to session hosts

Create and manage session host images

 create a gold image

 modify a session host image

 install language packs in Azure Virtual Desktop

 deploy a session host by using a custom image

 plan for image update and management

 create and use a Shared Image Gallery

 troubleshoot OS issues related to Azure Virtual Desktop

Manage Access and Security (10-15%)

Manage access

 plan and implement Azure roles and role-based access control (RBAC) for Azure Virtual Desktop

 manage local roles, groups and rights assignment on Azure Virtual Desktop session hosts

 configure user restrictions by using Azure AD group policies and AD policies Manage security

 plan and implement Conditional Access policies for connections to Azure Virtual Desktop

 plan and implement multifactor authentication in Azure Virtual Desktop

 manage security by using Azure Security Center

 configure Microsoft Defender Antivirus for session hosts

Manage User Environments and Apps (20-25%)

Implement and manage FSLogix

 plan for FSLogix

 install and configure FSLogix

 configure Profile Containers

 configure Cloud Cache

 migrate user profiles to FSLogix

Configure user experience settings

 configure Universal Print

 configure user settings through group policies and Endpoint Manager policies

 configure persistent and non-persistent desktop environments

 configure Remote Desktop Protocol (RDP) properties on a host pool

 configure session timeout properties

 troubleshoot user profile issues

 troubleshoot Azure Virtual Desktop clients

Install and configure apps on a session host

 configure dynamic application delivery by using MSIX App Attach

 implement application masking

 deploy an application as a RemoteApp

 implement and manage OneDrive for Business for a multi-session environment

 implement and manage Microsoft Teams AV Redirect

 implement and manage browsers and internet access for Azure Virtual Desktop sessions

 create and configure an application group

 troubleshoot application issues related to Azure Virtual Desktop

Monitor and Maintain an Azure Virtual Desktop Infrastructure (20-25%)

Plan and implement business continuity and disaster recovery

 plan and implement a disaster recovery plan for Azure Virtual Desktop

 design a backup strategy for Azure Virtual Desktop

 configure backup and restore for FSLogix user profiles, personal virtual desktop

infrastructures (VDIs), and golden images

Automate Azure Virtual Desktop management tasks

 configure automation for Azure Virtual Desktop

 automate management of host pools, session hosts, and user sessions by using

PowerShell and Azure Command-Line Interface (CLI)

 implement autoscaling in host pools

Monitor and manage performance and health

 monitor Azure Virtual Desktop by using Azure Monitor

 monitor Azure Virtual Desktop by using Azure Advisor

 customize Azure Monitor workbooks for Azure Virtual Desktop monitoring

 optimize session host capacity and performance

 manage active sessions and application groups

 monitor and optimize autoscaling results

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Prepare for DP-203: Data Engineering on Microsoft Azure Exam

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AWS distances itself from Google, Microsoft with ‘unique’ approach to generative AI

The cloud giant has gone on all in on its pick-and-choose ecosystem

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Image: Getty via Future

AWS has emphasised its commitment to providing customers with the widest range of AI tools, in a strategy that distances it from other hyperscale AI providers.

The firm, the third of the hyperscalers to fully enter the generative artificial intelligence (AI) foray, has distanced itself from Microsoft Azure and Google Cloud in its focus on an AI ecosystem that combines proprietary large language models (LLMs) with third-party models.

Microsoft has put OpenAI, and its popular GPT-n foundational models, at the center of its AI strategy, with GPT-4 being used throughout its Copilot branded productivity tools.

Google Cloud has committed to providing a range of AI models to its customers, but on a larger scale Google has chosen to pit itself against larger efforts to develop one-size-fits-all models such as PaLM 2 to power its search chatbot Bard.

With that in mind, the ability to access a range of models that are particularly good at one thing out of the box – with no further fine-tuning required – could let smaller firms embrace generative AI without having to settle for ‘jack of all trades’ models that lack specialisation.

An example of this can be seen in Amazon’s Titan LLMs, which come pre-trained to filter out profanity and hate speech.

In the keynote presentation at its AWS Summit London conference, the firm ran through an example of a digital marketing firm using a slew of AI models in order to push through a product campaign.

It used Anthropic’s Claude AI assistant for a product description, StableDiffusion to generate a product image, AI21’s Jurassic-1 LLM for social media copy, and AWS’ own Titan foundation model (FM) for SEO-optimised terms.

All models are accessible through AWS Bedrock, which provides customers with an ecosystem of AI and machine learning via APIs, enabling firms to pick and choose which data is passed to which model for tailored outputs.

Swami Sivasubramanian, VP of database, analytics, and ML at AWS, emphasised that in combination with Amazon’s Sagemaker, customers can embrace pre-trained models or train their own ML tools using its cloud architecture.

The cloud giant has been a vocal proponent of ‘democratising’ AI in recent months, having partnered with the data and machine learning platform Hugging Face to provide trusted open models.

Sivasubramanian rejected the idea that any one model could provide meet the use cases of every customer, all the time, in contrast to competitors such as Microsoft which has focused its efforts on powerful all-rounder LLMs such as GPT-4.

He noted that different models vary in their effectiveness when presented with tasks such as handling languages other than English, or in more subtle ways such as the tone of voice they tend to take.

“Even among text models, different ones tend to have different support for languages beyond English, for example,” he told ITPro.

“Different customers, depending on their use cases – price profile, performance – end up picking the right tool. That’s one of the reasons we designed Bedrock to provide access to the best-in-class models and provide the ability to customise these foundation models with their data.”

He acknowledged that at present, businesses lack concrete measures to benchmark AI models for specific use cases, and suggested that companies would instead come to know which models were best trained for what use cases through exposure over time.

“I expect as this space matures more and more, you’re going to find out which models tend to have better capabilities for different languages, different modalities, and even different use cases and so forth.

“Because these are a function of the areas and data they were trained on in the pre-training phase as well.”

This last point could prove especially useful for smaller firms, which may not have the expansive metadata necessary to tailor a model to fit their specific company brand to a significant degree.

AWS’ API-led approach could also allow firms to freely explore which models are best suited for their purposes on a trial-and-error basis.

ITPro spoke to Dr. Pandurang Kamat, chief technology officer at digital transformation firm Persistent, about the firm’s adoption of the AI code-generation tool Amazon CodeWhisperer, which operates similarly to Microsoft’s GitHub Copilot.

Persistent has been an AWS partner since 2012, and has indicated that it will roll out CodeWhisperer across its 16,000-strong organisation dependent on the effectiveness of its outputs.

“There are a lot of questions because of the way the product came into the public psyche, which was from a consumer-facing way”, said Kamat.

“So customers have questions like ‘hey, what happens to my data here? How is it being retained? What boundaries is it crossing? Is it being used to train the model itself?’”

He stated that Persistent would be trialing it on its projects to test the specific gains associated with CodeWhisperer before it could recommend it to its customers, in line with AWS’ approach of urging customers to try models out before fully committing to adopt them.

Amazon has claimed that CodeWhisperer has driven an average of 57% faster code adoption, and has made the individual tier of the solution free to use.

Microsoft’s New Azure Fluid Relay Service Now Generally Available

Recently, Microsoft announced its Azure Fluid Relay service's general availability (GA). This managed cloud offering enables web developers to use the Fluid Framework for building enterprise-grade collaborative applications.

Azure Fuild Relay service is built upon WebSockets, a full-duplex communication channel supported by modern web browsers. The innovation in Fluid Framework announced in 2019 at Microsoft's Build developer conference is that it supports distributed and synchronized shared state - so that creating a collaborative application is simplified. The only supported language currently for the Fluid Framework is TypeScript or JavaScript.

Developers can set up and run their applications with Azure Fluid Relay by provisioning the Azure Fluid Relay service in their Azure account and configuring the azure-client SDK package to connect their applications to the Azure Fluid Relay instance.

Source: https://devblogs.microsoft.com/microsoft365dev/announcing-general-availability-of-azure-fluid-relay-service/

The azure-client package has an AzureClient class that allows developers to interact with Fluid. The class contains the logic of connecting the Fluid container to the service. In addition, developers need to update their app’s configuration to connect to the Azure service with the correct configuration values.

import { AzureClient, AzureConnectionConfig } from "@fluidframework/azure-client"; const clientProps = { connection: { type: "remote", tenantId: "YOUR-TENANT-ID-HERE", tokenProvider: new AzureFunctionTokenProvider( "AZURE-FUNCTION-URL"+"/api/GetAzureToken", { userId: "test-user",userName: "Test User" } ), endpoint: "ENTER-SERVICE-DISCOVERY-URL-HERE", }, }; const azureClient = new AzureClient(clientProps);

Note that Microsoft also offers WebSocket-based services with Azure Web PubSub, a fully-managed service that supports native and serverless WebSockets and Azure SignalR, which allows developers to add real-time web functionality to applications. The latter is not designed to abstract distributed state in the same way as with Azure Fluid Relay. Furthermore, with Azure Web PubSub, similar to SignalR, without client or protocol requirements developers can bring their own WebSocket library.

In a Microsoft Developer blog post, Mathangi Chakrapani and Pranshu Kumar, both senior product marketing managers at Microsoft, wrote:

The service enables developers to build and deploy enterprise-ready collaborative applications that perform at industry-leading speed and scale. Azure Fluid Relay takes care of all details of Fluid collaboration while you as developers focus your attention on your app and end-user experience.

Currently, the Azure Fluid Relay service is available in around 10 Azure regions – half of which are in the US – on a pay-as-you-go basis. More details on pricing are available on the pricing page. In addition, developers can find more information and guidance through the documentation landing page.

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