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Exam Number : AZ-700
Exam Name : Designing and Implementing Microsoft Azure Networking Solutions
Vendor Name : Microsoft
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AZ-700 exam Format | AZ-700 Course Contents | AZ-700 Course Outline | AZ-700 exam Syllabus | AZ-700 exam Objectives


Title: Microsoft AZ-700 Designing and Implementing Microsoft Azure Networking Solutions

Test Detail:
The Microsoft AZ-700 exam is designed to validate the knowledge and skills required to design and implement networking solutions in Microsoft Azure. This certification is intended for IT professionals who work with Azure networking technologies and are responsible for designing and implementing Azure networking solutions.

Course Outline:
The Microsoft AZ-700 course provides participants with a comprehensive understanding of designing and implementing networking solutions in Microsoft Azure. The following is a general outline of the key areas covered in the certification program:

1. Designing Azure Virtual Networks:
- Designing virtual network topologies and subnets
- Implementing IP addressing and routing in Azure
- Configuring Azure Virtual Network Peering and Gateway Transit

2. Implementing Azure Virtual Network Connectivity:
- Implementing site-to-site VPN connections
- Configuring Azure ExpressRoute for private network connectivity
- Implementing Azure Virtual WAN for global network connectivity

3. Implementing Azure Load Balancer and Traffic Manager:
- Configuring Azure Load Balancer for high availability and load distribution
- Implementing Azure Traffic Manager for global traffic routing
- Configuring health probes and load balancing rules

4. Implementing Azure Network Security:
- Configuring network security groups (NSGs) for inbound and outbound traffic control
- Implementing Azure Firewall for network security and protection
- Configuring Azure DDoS Protection for safeguarding against DDoS attacks

5. Monitoring and Troubleshooting Azure Networking:
- Implementing Azure Network Watcher for network monitoring and diagnostics
- Using Azure Network Insights for troubleshooting and performance optimization
- Analyzing network traffic with Azure Network Analyzer

Exam Objectives:
The Microsoft AZ-700 exam assesses candidates' knowledge and skills in designing and implementing networking solutions in Microsoft Azure. The exam objectives include, but are not limited to:

1. Designing Azure Virtual Networks and subnets.
2. Implementing Azure Virtual Network connectivity using VPN and ExpressRoute.
3. Configuring Azure Load Balancer and Traffic Manager for load distribution and traffic routing.
4. Implementing network security measures using NSGs, Azure Firewall, and DDoS Protection.
5. Monitoring and troubleshooting Azure networking using Network Watcher and Network Insights.

Syllabus:
The Microsoft AZ-700 certification program typically includes comprehensive training provided by Microsoft or authorized training partners. The syllabus provides a breakdown of the syllabus covered throughout the course, including specific learning objectives and milestones. The syllabus may include the following components:

- Introduction to the Microsoft AZ-700 exam overview and certification process
- Designing Azure Virtual Networks and Subnets
- Implementing Azure Virtual Network Connectivity
- Implementing Azure Load Balancer and Traffic Manager
- Implementing Azure Network Security
- Monitoring and Troubleshooting Azure Networking
- exam Preparation and Practice Tests
- Final Microsoft AZ-700 Certification Exam



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Microsoft Azure certification

 

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No result found, try new keyword!Microsoft Azure, and much more. Prepare yourself to pass Microsoft’s certification exams to become a Microsoft Certified Expert. For a limited time, you can get The Complete 2024 Microsoft Tech ...

What Microsoft’s custom silicon means for Azure

The history of modern software development has been a dance between what hardware can deliver and what software demands. Over the decades, the steps in this dance have moved us from the original Intel 8086, which they now consider very basic functionality, to today’s multi-faceted processors, which provide virtualization support, end-to-end access to encrypted memory and data, and extended instruction sets that power the most demanding application stacks.

This dance swings from side to side. Sometimes their software has to stretch to meet the capabilities of a new generation of silicon, and sometimes it has to squeeze out every last ounce of available performance. Now, we’re finally seeing the arrival of a new generation of hardware that mixes familiar CPUs with new system-level accelerators that provide the ability to run complex AI models on both client hardware and servers, both on premises and in the public cloud.

You’ll find AI accelerators not only in the familiar Intel and AMD processors but also in Arm’s latest generation of Neoverse server-grade designs, which mix those features with low power demands (as do Qualcomm’s mobile and laptop offerings). It’s an attractive combination of features for hyperscale clouds like Azure, where low power and high density can help keep costs down while allowing growth to continue.

At the same time, system-level accelerators promise an interesting future for Windows, allowing us to use on-board AI assistants as an alternative to the cloud as Microsoft continues to Boost the performance of its Phi series of small language models.

Azure Boost: Silicon for virtualization offload

Ignite 2023 saw Microsoft announce its own custom silicon for Azure, hardware that should start rolling out to customers in 2024. Microsoft has been using custom silicon and FPGAs in its own services for some time now. The use of Zipline hardware compression and Project Brainwave FPGA-based AI accelerators are good examples. The most latest arrival is Azure Boost, which offloads virtualization processes from the hypervisor and host OS to accelerate storage and networking for Azure VMs. Azure Boost also includes the Cerberus on-board supply chain security chipset.

Azure Boost is intended to deliver your virtual machine workloads access to as much of the available CPU as possible. Instead of using CPU to compress data or manage security, dedicated hardware takes over, allowing Azure to run more customer workloads on the same hardware. Running systems at high utilization is key to the economics of the public cloud, and any investment in hardware will quickly be paid off.

Maia 100: Silicon for large language models

Large language models (and generative AI generally) show the importance of dense compute, with OpenAI using Microsoft’s GPU-based supercomputer to train its GPT models. Even on a system like Microsoft’s, big foundation models like GPT-4 require months of training, with more than a trillion parameters. The next generation of LLMs will need even more compute, both for training and for operation. If we’re building grounded applications around those LLMs, using Retrieval Augmented Generation, we’ll need additional capacity to create embeddings for their source content and to provide the underlying vector-based search.

GPU-based supercomputers are a significant investment, even when Microsoft can recoup some of the capital costs from subscribers. Operational costs are also large, with hefty cooling requirements on top of power, bandwidth, and storage. So, they might expect those resources to be limited to very few data centers, where there’s sufficient space, power, and cooling.

But if large-scale AI is to be a successful differentiator for Azure, versus competitors such as AWS and Google Cloud, it will need to be available everywhere and it will need to be affordable. That will require new silicon (for both training and inferencing) that can be run at higher densities and at lower power than today’s GPUs.

Looking back at Azure’s Project Brainwave FPGAs, these used programmable silicon to implement key algorithms. While they worked well, they were single-purpose devices that acted as accelerators for specific machine learning models. You could develop a variant that supported the complex neural networks of a LLM, but it would need to implement a massive array of simple processors to support the multi-dimensional vector arithmetic that drives these semantic models. That’s beyond the capabilities of most FPGA technologies.

Vector processing is something that modern GPUs are very good at (not surprisingly, as many of the original architects began their careers developing vector processing hardware for early supercomputers). A GPU is basically an array of simple processors that work with matrices and vectors, using technologies like Nvidia’s CUDA to provide access to linear algebra functions that aren’t commonly part of a CPU’s instruction set. The resulting acceleration lets us build and use modern AI models like LLMs.

Microsoft’s new custom AI accelerator chip, Maia 100, is designed for both training and inference. Building on lessons learned running OpenAI workloads, Maia is intended to fit alongside existing Azure infrastructure, as part of a new accelerator rack unit that sits alongside existing compute racks. With over 100 billion transistors delivered by a five-nanometer process, the Maia 100 is certainly a very large and very dense chip, with much more compute capability than a GPU.

The development of the Maia was refined alongside OpenAI’s models, and uses a new rack design that includes custom liquid-based cooling elements. That last part is key to delivering AI workloads to more than the largest Azure data centers. Adding liquid cooling infrastructure is expensive, so putting it in the Maia 100 racks ensures that it can be dropped into any data center, anywhere in the world.

Installing Maia 100 racks does require readjusting rack spacing, as the cooling system makes them larger than Azure’s typical 21-inch racks, which are sized for Open Compute Project servers. In addition to the liquid cooling hardware, the extra space is used for 4.8 Tb high-bandwidth interconnects, essential for pushing large amounts of data between CPUs and accelerators.

There are still questions about how applications will get to use the new chips. Absent additional details, it’s likely that they’ll run Microsoft-provided AI models, like OpenAI’s and Hugging Face’s, as well as their own Cognitive Services and the Phi small language models. If they become available to train your own models, expect to see a new class of virtual machines alongside the current range of GPU options in Azure AI Studio.

Cobalt 100: Azure’s own Arm processor

Alongside the unveiling of Maia, Microsoft announced its own Arm server processor, the Cobalt 100. This is a 128-core 64-bit processor, designed to support high-density, low-power applications, based on Arm’s Neoverse reference design. Azure is already using Arm processors for some of its platform services, and Cobalt 100 is likely to support these and more services, rather than being used for infrastructure as a service.

There’s no need to know if your Azure App Service code is running on Intel, AMD, or Arm, as long as it performs well and your users get the results they expect. They can expect to see Cobalt processors running internet-facing services, where density and power efficiency are important requirements, as well as hosting elements of Azure’s content delivery network outside of its main data centers.

Microsoft describes its silicon engineering as a way of delivering a “systems approach” to its Azure data centers, with end-to-end support from its initial storage and networking offerings to its own compute services. And it’s not only Azure. Better silicon is coming to Windows too, as NPU-enabled processors from Intel and Qualcomm start to arrive in 2024’s desktops and laptops. After many years of software leading hardware, it will be interesting to see how they can push these new platforms to their limits with code.

Copyright © 2024 IDG Communications, Inc.


LinkedIn shelved planned move to Microsoft Azure, opting to keep physical data centers

Microsoft CEO Satya Nadella arrives at federal court in Washington on Oct. 2, 2023.

Nathan Howard | Bloomberg | Getty Images

LinkedIn has set aside an effort to relocate its data center technology out of its physical facilities and into Microsoft's Azure cloud, according to people familiar with the matter.

The decision not to proceed with the project, code-named "Blueshift," marks a major reversal for LinkedIn, which announced its plan to move to Azure in 2019, three years after Microsoft acquired the company for $27 billion. LinkedIn had been using Azure for specific tasks.

The U-turn represents a setback for Microsoft, which is chasing Amazon Web Services in the lucrative cloud infrastructure market and has been counting on cloud technology and services to fuel much of its growth. Microsoft CEO Satya Nadella ran the cloud business before elevation to his current job in 2014.

Mohak Shroff, LinkedIn's vice president of engineering, wrote in a 2019 blog post announcing Blueshift that "moving to Azure will deliver us access to a wide array of hardware and software innovations, and unprecedented global scale."

Staffers started to learn of the decision not to follow through with the Azure migration last year, said the sources, who asked not to be named because of confidentiality. Executives stressed that the project was being put on hold, rather than getting canceled altogether, they said.

In a memo to research and development employees in June 2022, LinkedIn Chief Technology Officer Raghu Hiremagalur said LinkedIn would continue to use some Azure services and will "focus their efforts on scaling and innovating their on-prem infrastructure." A different internal document, viewed by CNBC, says LinkedIn and Microsoft together agreed to hold off on trying to get LinkedIn's website running on Azure.

"With the incredible demand Azure is seeing and the growth of their platform, we've decided to pause their planned migration of LinkedIn to allocate resources to external Azure customers," Hiremagalur wrote in his memo.  

A LinkedIn spokesperson confirmed that the Microsoft subsidiary changed direction on Blueshift and said LinkedIn continues to use Azure.

"We are using both Azure to complement their infrastructure needs and further investing in their data centers," the spokesperson said in an email. "This includes their running 100 employee-facing applications on Azure, leveraging Azure FrontDoor and ongoing work to consolidate their datacenter locations that are currently spread across multiple buildings under a single roof. Azure has been crucial to support and scale collaboration and productivity for their teams and to deliver value to their members." 

Azure Front Door is a content delivery network that keeps information stored in a variety of places around the world so it can quickly be sent to devices.

Issues with the planned migration arose from LinkedIn attempting to use its own software tools instead of those readily available on Azure, one of the people said. LinkedIn is in the process of constructing an additional data center to handle its computing needs, the person said.

Under the leadership of Nadella, Microsoft has moved some of its acquired assets to Azure, including GitHub and Minecraft developer Mojang.

More recently, Azure has gained attention because of Microsoft's investment in OpenAI, which uses Azure infrastructure for running the large language models powering ChatGPT and other products. Nadella told Wired that he first saw the GPT-4 LLM from OpenAI in the summer of 2022, a few months before OpenAI released the ChatGPT chatbot.

Microsoft said in October that third-quarter revenue from Azure and other cloud services grew 29%, while LinkedIn revenue was up 8%. LinkedIn said in November that it had reached 1 billion members.

WATCH: January's hiring rate is expected to be down from previous years, says LinkedIn's Karin Kimbrough

January's hiring rate is expected to be down from previous years, says LinkedIn's Karin Kimbrough

watch now


 


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