 Microsoft AZ-500 : Microsoft Azure Security Technologies ExamExam Dumps Organized by Huiliang
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Exam Number : AZ-500
Exam Name : Microsoft Azure Security Technologies
Vendor Name : Microsoft
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AZ-500 test
Format | AZ-500 Course Contents | AZ-500 Course Outline | AZ-500 test
Syllabus | AZ-500 test
Objectives
Manage identity and access (20-25%)
Configure Azure Active Directory for workloads
=> create App Registration
=> configure App Registration permission scopes
=> manage App Registration permission consent
=> configure Multi-Factor Authentication settings
=> manage Azure AD directory groups
=> manage Azure AD users
=> install and configure Azure AD Connect
=> configure authentication methods
=> implement Conditional Access policies
=> configure Azure AD identity protection
Configure Azure AD Privileged Identity Management
=> monitor privileged access
=> configure Access Reviews
=> activate Privileged Identity Management
Configure Azure tenant security
=> transfer Azure subscriptions between Azure AD tenants
=> manage API access to Azure subscriptions and resources
Implement platform protection (35-40%)
Implement network security
=> configure virtual network connectivity
=> configure Network Security Groups (NSGs)
=> create and configure Azure Firewall
=> create and configure Azure Front Door service
=> create and configure application security groups
=> configure remote access management
=> configure baseline
=> configure resource firewall
Implement host security
=> configure endpoint security within the VM
=> configure VM security
=> harden VMs in Azure
=> configure system updates for VMs in Azure
=> configure baseline
Configure container security
=> configure network
=> configure authentication
=> configure container isolation
=> configure AKS security
=> configure container registry
=> implement vulnerability management
Implement Azure Resource management security
=> create Azure resource locks
=> manage resource group security
=> configure Azure policies
=> configure custom RBAC roles
=> configure subscription and resource permissions
Manage security operations (15-20%)
Configure security services
=> configure Azure Monitor
=> configure diagnostic logging and log retention
=> configure vulnerability scanning
Configure security policies
=> configure centralized policy management by using Azure Security Center
=> configure Just in Time VM access by using Azure Security Center
Manage security alerts
=> create and customize alerts
=> review and respond to alerts and recommendations
=> configure a playbook for a security event by using Azure Security Center
=> investigate escalated security incidents
Secure data and applications (25-30%)
Configure security policies to manage data
=> configure data classification
=> configure data retention
=> configure data sovereignty
Configure security for data infrastructure
=> enable database authentication
=> enable database auditing
=> configure Azure SQL Database Advanced Threat Protection
=> configure access control for storage accounts
=> configure key management for storage accounts
=> configure Azure AD authentication for Azure Storage
=> configure Azure AD Domain Services authentication for Azure Files
=> create and manage Shared Access Signatures (SAS)
=> configure security for HDInsight
=> configure security for Cosmos DB
=> configure security for Azure Data Lake
Configure encryption for data at rest
=> implement Azure SQL Database Always Encrypted
=> implement database encryption
=> implement Storage Service Encryption
=> implement disk encryption
Configure application security
=> configure SSL/TLS certs
=> configure Azure services to protect web apps
=> create an application security baseline
Configure and manage Key Vault
=> manage access to Key Vault
=> manage permissions to secrets, certificates, and keys
=> configure RBAC usage in Azure Key Vault
=> manage certificates
=> manage secrets
=> configure key rotation
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Microsoft Technologies questions
raise your enterprise data technology and strategy at transform 2021.
one of the biggest highlights of construct, Microsoft’s annual application construction convention, changed into the presentation of a tool that makes use of deep discovering to generate source code for workplace applications. The tool uses GPT-3, a massive language mannequin developed through OpenAI last 12 months and made obtainable to opt for builders, researchers, and startups in a paid software programming interface.
Many have touted GPT-three as the next-technology synthetic intelligence technology as a way to herald a brand new breed of purposes and startups. when you consider that GPT-3’s free up, many developers have found enjoyable and imaginative makes use of for the language mannequin. And a couple of startups have declared that they may be using GPT-three to construct new or augment latest products. however making a ecocnomic and sustainable company around GPT-three is still a challenge.
Microsoft’s first GPT-three-powered product provides critical suggestions about the company of significant language models and the way forward for the tech massive’s deepening relation with OpenAI.
a few-shot discovering model that should be first-rate-tuned?
Above: Microsoft uses GPT-three to translate herbal language commands to facts queries
photograph credit score: Khari Johnson / VentureBeat
in accordance with the Microsoft weblog, “for example, the new AI-powered elements will enable an worker building an e-commerce app to explain a programming goal using conversational language like ‘find products where the identify starts with “children.”’ A high-quality-tuned GPT-three mannequin [emphasis mine] then offers decisions for transforming the command right into a Microsoft vigour Fx formula, the open source programming language of the vigor Platform.”
I didn’t discover technical particulars on the exceptional-tuned edition of GPT-three Microsoft used. however there are generally two motives you could quality-tune a deep gaining knowledge of mannequin. in the first case, the mannequin doesn’t perform the goal task with the desired precision, so that you deserve to exceptional-tune it via practising it on examples for that particular assignment.
in the 2nd case, your mannequin can function the meant project, however is computationally inefficient. GPT-three is a really huge deep gaining knowledge of mannequin with 175 billion parameters, and the expenses of working it are large. therefore, a smaller edition of the mannequin can also be optimized to operate the code-technology project with the equal accuracy at a fraction of the computational can charge. a possible tradeoff could be that the mannequin will perform poorly on different initiatives (similar to question-answering). however in Microsoft’s case, the penalty may be beside the point.
In both case, a first-class-tuned version of the deep learning mannequin looks to be at odds with the original conception discussed in the GPT-3 paper, aptly titled, “Language models are Few-Shot newcomers.”
here’s a quote from the paper’s abstract: “right here we show that scaling up language models greatly improves project-agnostic, few-shot performance, occasionally even accomplishing competitiveness with prior state-of-the-art finetuning approaches.” This really means that, in case you build a big enough language mannequin, you should be capable of perform many initiatives devoid of the deserve to reconfigure or alter your neural network.
So, what’s the factor of the few-shot computing device learning model that have to be satisfactory-tuned for brand new tasks? here is where the worlds of scientific analysis and applied AI collide.
academic research vs commercial AI
There’s a transparent line between tutorial research and business product development. In tutorial AI analysis, the aim is to push the boundaries of science. here is precisely what GPT-3 did. OpenAI’s researchers confirmed that with sufficient parameters and practising statistics, a single deep learning mannequin could operate a few initiatives devoid of the want for retraining. and they have Verified the mannequin on a number of everyday natural language processing benchmarks.
but in business product development, you’re now not operating in opposition t benchmarks reminiscent of GLUE and SQuAD. You ought to resolve a specific difficulty, solve it ten times more advantageous than the incumbents, and be able to run it at scale and in a cost-effective manner.
hence, when you have a huge and costly deep getting to know mannequin that can function ten different tasks at ninety percent accuracy, it’s a very good scientific fulfillment. however when there are already ten lighter neural networks that operate every of these initiatives at 99 percent accuracy and a fraction of the cost, then your jack-of-all-trades mannequin are not in a position to compete in a income-pushed market.
here’s a captivating quote from Microsoft’s weblog that confirms the challenges of applying GPT-3 to precise business complications: “This discovery of GPT-three’s immense capabilities exploded the boundaries of what’s possible in natural language discovering, observed Eric Boyd, Microsoft corporate vice chairman for Azure AI. however there have been nonetheless open questions about even if such a large and sophisticated mannequin may well be deployed can charge-comfortably at scale to satisfy actual-world enterprise needs [emphasis mine].”
and people questions were answered with the optimization of the model for that selected task. in view that Microsoft wanted to resolve a really specific problem, the entire GPT-three mannequin would be an overkill that could waste high priced components.
hence, the undeniable vanilla GPT-three is greater of a scientific fulfillment than a legitimate platform for product building. but with the right substances and configuration, it will probably become a beneficial device for market differentiation, which is what Microsoft is doing.
Microsoft’s potential
In a great world, OpenAI would have launched its own items and generated profits to fund its personal research. but the fact is, establishing a ecocnomic product is tons greater problematic than releasing a paid API service, even though your business’s CEO is Sam Altman, the previous President of Y Combinator and a product building legend.
And here's why OpenAI enrolled the help of Microsoft, a choice with the intention to have lengthy-time period implications for the AI research lab. In July 2019, Microsoft made a $1 billion investment in OpenAI—with some strings attached.
From the OpenAI blog put up that declared the Microsoft investment: “OpenAI is producing a sequence of increasingly powerful AI applied sciences, which requires loads of capital for computational energy. probably the most obtrusive solution to cover charges is to build a product, but that could imply altering our center of attention [emphasis mine]. in its place, we intend to license a few of our pre-AGI technologies, with Microsoft fitting our favored companion for commercializing them.”
by myself, OpenAI would have a hard time discovering a method to enter an existing market or create a new market for GPT-three.
even so, Microsoft already has the pieces required to shortcut OpenAI’s path to profitability. Microsoft owns Azure, the second-largest cloud infrastructure, and it is in an appropriate position to subsidize the fees of coaching and working OpenAI’s deep learning fashions.
however more importantly—and here's why I believe OpenAI selected Microsoft over Amazon—is Microsoft’s reach across distinctive industries. hundreds of corporations and hundreds of thousands of clients are the use of Microsoft’s paid functions reminiscent of workplace, teams, Dynamics, and energy Apps. These applications give best platforms to combine GPT-three.
Microsoft’s market competencies is thoroughly evident in its first software for GPT-3. it's a really basic use case targeted at a non-technical viewers. It’s now not supposed to do complicated programming logic. It simply converts herbal language queries into data formulas in vigour Fx.
This trivial software is beside the point to most pro builders, who will locate it much less demanding to without delay classification their queries than describe them in prose. however Microsoft has a number of customers in non-tech industries, and its power Apps are built for clients who don’t have any coding journey or are studying to code. For them, GPT-three could make a huge difference and help lessen the barrier to setting up elementary functions that solve business problems.
Microsoft has yet another ingredient working to its abilities. It has secured unique entry to the code and architecture of GPT-3. whereas different organizations can only have interaction with GPT-3 in the course of the paid API, Microsoft can customize it and integrate it without delay into its purposes to make it productive and scalable.
through making the GPT-three API obtainable to startups and developers, OpenAI created an ambiance to find all sorts of functions with enormous language fashions. meanwhile, Microsoft become sitting lower back, staring at all the distinctive experiments with transforming into activity.
The GPT-3 API definitely served as a product research task for Microsoft. some thing use case any company finds for GPT-three, Microsoft could be in a position to do it faster, more cost-effective, and with better accuracy due to its exclusive access to the language model. This gives Microsoft a unique abilities to dominate most markets that take form round GPT-3. And this is why I suppose most groups that are constructing items on precise of the GPT-three API are doomed to fail.
The OpenAI Startup Fund
Above: Microsoft CEO Satya Nadella (left) and OpenAI CEO Sam Altman (appropriate) at Microsoft build 2021
photo credit: Khari Johnson / VentureBeat
And now, Microsoft and OpenAI are taking their partnership to the next stage. at the build conference, Altman declared a $a hundred million fund, the OpenAI Startup Fund, wherein it will put money into early-stage AI organizations.
“We plan to make big early bets on a comparatively small variety of businesses, probably now not more than 10,” Altman pointed out in a prerecorded video performed on the conference.
What type of organizations will the fund put money into? “We’re hunting for startups in fields where AI can have essentially the most profound tremendous have an impact on, like healthcare, local weather exchange, and training,” Altman talked about, to which he introduced, “We’re also enthusiastic about markets the place AI can power large leaps in productivity like personal counsel and semantic search.” the primary part appears to be according to OpenAI’s mission to use AI for the betterment of humanity. however the 2d half seems to be the class of income-generating applications that Microsoft is exploring.
also from the fund’s web page: “The fund is managed by way of OpenAI, with investment from Microsoft and other OpenAI partners. moreover capital, groups within the OpenAI Startup Fund will get early entry to future OpenAI methods, help from our crew, and credit on Azure.”
So, basically, it appears like OpenAI is fitting a advertising proxy for Microsoft’s Azure cloud and will help spot AI startups that might qualify for acquisition via Microsoft sooner or later. this could deepen OpenAI’s partnership with Microsoft and ensure the lab continues to get funding from the tech enormous. but it surely will also take OpenAI a step closer towards fitting a commercial entity and eventually a subsidiary of Microsoft. How this can have an effect on the research lab’s long-time period purpose of scientific analysis on synthetic widespread intelligence remains an open question.
Ben Dickson is a software engineer and the founding father of TechTalks. He writes about technology, company, and politics.
This story at the beginning looked on Bdtechtalks.com. Copyright 2021
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