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Microsoft AZ-303 : Microsoft Azure Architect Technologies Exam

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Exam Number : AZ-303
Exam Name : Microsoft Azure Architect Technologies
Vendor Name : Microsoft
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EXAM NAME : Microsoft Azure Architect Technologies

Candidates for this test should have subject matter expertise in designing and implementing solutions that run on Microsoft Azure, including aspects like compute, network, storage, and security. Candidates should have intermediate-level skills for administering Azure. Candidates should understand Azure development and DevOps processes.

Responsibilities for an Azure Solution Architect include advising stakeholders and translating business requirements into secure, scalable, and reliable cloud solutions.

An Azure Solution Architect partners with cloud administrators, cloud DBAs, and clients to implement solutions.

A candidate for this test should have advanced experience and knowledge of IT operations, including networking, virtualization, identity, security, business continuity, disaster recovery, data platform, budgeting, and governancethis role should manage how decisions in each area affect an overall solution. In addition, this role should have expert-level skills in Azure administration and have experience with Azure development and DevOps processes.

- Implement and monitor an Azure infrastructure (50-55%)
- Implement management and security solutions (25-30%)
- Implement solutions for apps (10-15%)
- Implement and manage data platforms (10-15%)

Implement and Monitor an Azure Infrastructure (50-55%)
Implement cloud infrastructure monitoring
=> monitor security
=> monitor performance
=> monitor health and availability
=> monitor cost
=> configure advanced logging
=> configure logging for workloads initiate automated responses by using Action Groups
=> configure and manage advanced alerts Implement storage accounts
=> select storage account options based on a use case
=> configure Azure Files and blob storage
=> configure network access to the storage account
=> implement Shared Access Signatures and access policies
=> implement Azure AD authentication for storage
=> manage access keys
=> implement Azure storage replication
=> implement Azure storage account failover
Implement VMs for Windows and Linux
=> configure High Availability
=> configure storage for VMs
=> select virtual machine size
=> implement Azure Dedicated Hosts
=> deploy and configure scale sets
=> configure Azure Disk Encryption
Automate deployment and configuration of resources
=> save a deployment as an Azure Resource Manager template
=> modify Azure Resource Manager template
=> evaluate location of new resources
=> configure a virtual disk template
=> deploy from a template
=> manage a template library
=> create and execute an automation runbook
Implement virtual networking
=> implement VNet to VNet connections
=> implement VNet peering
Implement Azure Active Directory
=> add custom domains
=> configure Azure AD Identity Protection
=> implement self-service password reset
=> implement Conditional Access including MFA
=> configure user accounts for MFA
=> configure fraud alerts
=> configure bypass options
=> configure Trusted IPs
=> configure verification methods
=> implement and manage guest accounts
=> manage multiple directories
Implement and manage hybrid identities
=> install and configure Azure AD Connect
=> identity synchronization options
=> configure and manage password sync and password writeback
=> configure single sign-on
=> use Azure AD Connect Health

Implement Management and Security Solutions (25-30%)
Manage workloads in Azure
=> migrate workloads using Azure Migrate
=> implement Azure Backup for VMs
=> implement disaster recovery
=> implement Azure Update Management
Implement load balancing and network security
=> implement Azure Load Balancer
=> implement an application gateway
=> implement a Web Application Firewall
=> implement Azure Firewall
=> implement Azure Firewall Manager
=> implement the Azure Front Door Service
=> implement Azure Traffic Manager
=> implement Network Security Groups and Application Security Groups
=> implement Bastion
Implement and manage Azure governance solutions
=> create and manage hierarchical structure that contains management groups,subscriptions and resource groups
=> assign RBAC roles
=> create a custom RBAC role
=> configure access to Azure resources by assigning roles
=> configure management access to Azure
=> interpret effective permissions
=> set up and perform an access review
=> implement and configure an Azure Policy
=> implement and configure an Azure Blueprint
Manage security for applications
=> implement and configure KeyVault
=> implement and configure Managed Identities
=> register and manage applications in Azure AD

Implement Solutions for Apps (10-15%)
Implement an application infrastructure
=> create and configure Azure App Service
=> create an App Service Web App for Containers
=> create and configure an App Service plan
=> configure an App Service
=> configure networking for an App Service
=> create and manage deployment slots
=> implement Logic Apps
=> implement Azure Functions
Implement container-based applications
=> create a container image
=> configure Azure Kubernetes Service
=> publish and automate image deployment to the Azure Container Registry
=> publish a solution on an Azure Container Instance

Implement and Manage Data Platforms (10-15%)
Implement NoSQL databases
=> configure storage account tables
=> select appropriate CosmosDB APIs
=> set up replicas in CosmosDB
Implement Azure SQL databases
=> configure Azure SQL database settings
=> implement Azure SQL Database managed instances
=> configure HA for an Azure SQL database
=> publish an Azure SQL database

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Microsoft Technologies Study Guide

text autocompletion techniques intention to ease their lives, however there are hazards | AZ-303 practice test and Test Prep

Did you leave out a session from the future of Work Summit? Head over to our way forward for Work Summit on-demand library to circulation.

if you’ve written a text message or email lately, chances are high AI advised to you distinct synonyms, phrases, or the way to conclude a sentence. the upward thrust of AI-powered autosuggestion equipment like Google’s smart Compose has coincided with the digital transformation of business communications, which now are living normally online. It’s estimated that the standard worker replies to about 40 emails daily and sends more than 200 Slack messages per week.

Messaging threatens to devour an expanding portion of the workday, with Adobe pegging the period of time that worker's spend answering emails at 15.5 hours per week. The regular assignment switching is a loss of life knell for productiveness, which experiences show merits from uninterrupted work. analysis from the college of California and Humboldt university discovered that laborers can lose up to 23 minutes on a job every time they’re interrupted, additional lengthening the workday.

Autosuggestion equipment promise to save time with the aid of streamlining message-writing and replying. Google’s wise Reply, for instance, suggests quick responses to emails that’d continually take minutes to category out. but the AI in the back of these equipment has shortcomings that might introduce biases or have an impact on the language utilized in messaging in undesirable ways.

The boom in autosuggestion and textual content autocompletion

Predictive textual content isn’t a brand new expertise. some of the first extensively attainable examples, T9, which enables words to be formed from a single keypress for every letter, got here commonplace on many cellphones in the late ’90s. but the creation of more refined, scalable AI suggestions in language led to leaps within the best — and breadth — of autosuggestion tools.

In 2017, Google launched wise Reply in Gmail, which the business later brought to other Google features including Chat and third-birthday party apps. in accordance with Google, the AI behind sensible Reply generates reply information “in keeping with the full context of a dialog,” not only a single message — ostensibly resulting in advice that are extra timely and important. smart Compose, which means finished sentences in emails, arrived in Gmail a 12 months later and Google docs quickly later on. the same function referred to as recommended replies came to Microsoft Outlook in 2018 and groups in 2020.

The know-how behind the new crop of autosuggestion tools — which some tutorial circles refer to as “AI-mediated communication” — is leaps beyond what existed in the ’90s. as an example, the AI mannequin underpinning sensible Compose was created the usage of billions of examples of emails and runs within the cloud on customized accelerator hardware. meanwhile, wise Reply — which served as the groundwork for smart Compose — takes a “hierarchical method” to counsel, impressed by way of how people be aware languages and ideas.

Microsoft Smart Reply

Microsoft Smart Reply

Above: Outlook’s sensible Reply uses deep researching models trained in Azure desktop gaining knowledge of.

photo credit: Microsoft

“The content material of language is deeply hierarchical, reflected within the constitution of language itself …” Google analysis scientist Brian Strope and engineering director Ray Kurzweil clarify in a weblog put up. “consider the message, ‘That wonderful person at the cafe they like gave me a glance.’ … In proposing an appropriate response to this message they could accept as true with the meaning of the be aware ‘glance,’ which is doubtlessly ambiguous. was it a positive gesture? if so, they might respond, ‘Cool!’ Or turned into it a bad gesture? if so, does the field say the rest about how the author felt about the poor change? loads of assistance concerning the world, and an skill to make reasoned judgments, are necessary to make subtle distinctions. Given sufficient examples of language, a desktop studying approach can find a lot of these delicate distinctions. ”

however as with all technologies, even the most ready autosuggestion tools are at risk of flaws that crop up all over the building — and deployment — system.

In December 2016, it changed into published that Google Search’s autocomplete feature recommended hateful and offensive endings for selected search phrases, like “are jews evil?” for the phrase “are jews”. based on the company, at fault become an algorithmic system that updates information based on what other users have looked for these days. while Google ultimately carried out a fix, it took a number of more years for the company to block autocompletion advice for controversial political statements including false claims about voting necessities and the legitimacy of electoral methods.

smart Reply has been found to offer the “grownup wearing turban” emoji in keeping with a message that included a gun emoji. And Apple’s autocompletion on iOS in the past counseled simplest male emoji for executive roles including CEO, COO, and CTO.

Biased records

Flaws in autocompletion and autosuggestion systems frequently come up from biased data. The hundreds of thousands to billions of examples from which the techniques study can be tainted with text from poisonous sites that associate definite genders, races, ethnicities, and religions with hurtful ideas. Illustrating the problem, Codex, a code-producing mannequin developed through research lab OpenAI, can be brought on to put in writing “terrorist” when fed the be aware “Islam.” an additional giant language model from AI startup Cohere tends to associate men and ladies with stereotypically “male” and “feminine” occupations, like “male scientist” and “female housekeeper.”

Smart Compose for Google Docs

Smart Compose for Google Docs

Above: smart Compose for Google doctors.

Annotations within the statistics can introduce new problems — or exacerbate latest ones. because many fashions learn from labels that talk no matter if a observe, sentence, paragraph or document has definite qualities, like a good or negative sentiment, corporations and researchers recruit teams of human annotators to label examples, customarily from crowdsourcing platforms like Amazon Mechanical Turk. These annotators bring their own units of views — and biases — to the desk.

In a look at from the Allen Institute for AI, Carnegie Mellon, and the college of Washington, scientists discovered that labelers usually tend to annotate phrases within the African American English (AAE) dialect extra poisonous than conventional American English equivalents — regardless of their being understood as non-toxic with the aid of AAE audio system. Jigsaw, the company working below Google dad or mum enterprise Alphabet to tackle cyberbullying and disinformation, has drawn an identical conclusions in its experiments. Researchers at the enterprise have discovered differences in the annotations between labelers who self-determine as African american citizens and individuals of LGBTQ+ neighborhood versus annotators who don’t establish as either of those groups.

once in a while, the bias is intentional — a rely of vernacular exchange-offs. as an instance, author, a startup establishing an AI assistant for content material era, says that it prioritizes “company English” in its writing information. CEO may Habib gave the illustration of the “habitual be” in AAVE, a verb demanding that doesn’t exist in any other vogue of English.

“when you consider that [the habitual be] traditionally hasn’t been utilized in business English, and therefore doesn’t reveal up in excessive frequency in their datasets, they would appropriate ‘Y’all be doing some unusual issues out right here’ to ‘Y’all are performing some ordinary things out right here,'” Habib told VentureBeat by the use of electronic mail. “[That said,] they did manually make sure that vernacular-based mostly greetings and signal-offs would no longer be flagged by using writer. Some vernacular is more gender-neutral than formal business English, [for instance,] so is extra modern and on-company for businesses.”

Influencing writing

When biases — intentional or no longer — make it into autocompletion and autosuggestion systems, they can exchange the style that they write. The enormous scale at which these methods function makes them difficult (if not impossible) to absolutely stay away from. wise Reply turned into responsible for 10% of all Gmail replies despatched from smartphones in 2016.

in a single of the more complete audits of autocompletion equipment, a group of Microsoft researchers carried out interviews with volunteers who had been informed to supply their thoughts on auto-generated replies in Outlook. The interviewees discovered some of the replies to be over-high quality, wrong of their assumptions about lifestyle and gender, and too impolite for certain contexts, like corporate correspondences. Even still, experiments all through the analyze confirmed that clients were extra more likely to want short, high-quality, and polite replies counseled with the aid of Outlook.

Google SmartReply YouTube

Google SmartReply YouTube

A separate Harvard study discovered that after americans writing about a restaurant were offered with “advantageous” autocomplete information, the resulting reports tended to be extra effective than if they had been offered with bad assistance. “It’s enjoyable to consider about how predictive textual content techniques of the long run may assist people become much more constructive writers, however they additionally need transparency and accountability to protect in opposition t assistance that may be biased or manipulated,” Ken Arnold, a researcher at Harvard’s college of Engineering and applied Sciences who changed into involved within the study, informed the BBC.

If there’s an all-encompassing solution to the difficulty of dangerous autocompletion, it hasn’t been found yet. Google opted to effortlessly block gender-based mostly pronoun guidance in smart Compose because the equipment proved to be a bad predictor of recipients’ sexes and gender identities. Microsoft’s LinkedIn additionally avoids gendered pronouns in smart Replies, its predictive messaging device, to steer clear of advantage mistakes.

The coauthors of the Microsoft analyze warn that if gadget designers don’t proactively tackle the shortcomings in autocompletion applied sciences, they’ll run the possibility of not simplest offending users but inflicting them to distrust the techniques. “device designers should explore personalization suggestions at the individual and social community level, consider how cultural values and societal biases may well be perpetuated with the aid of their programs, and explore social interaction modeling as a way to start addressing the barriers and concerns,” they wrote. “[O]ur findings indicate that current textual content advice techniques for e-mail and different [like] applied sciences remain insufficiently nuanced to reflect the subtleties of real-world social relationships and conversation wants. “

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