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Exam Number : AI-102
Exam Name : Designing and Implementing a Microsoft Azure AI Solution
Vendor Name : Microsoft
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Exam Number: AI-102

Exam Name : Designing and Implementing a Microsoft Azure AI Solution



Exam TOPICS



The Cisco Customer Success Manager (DTCSM) v2.1 course gives you the confidence and competence to fulfill the Customer Success Manager (CSM) role successfully, helping your customers realize value from their solutions and achieve their business outcomes. The course offers experiential learning through practical exercises using situations based on real-life use cases and case studies. In this highly interactive course, you can practice and gain confidence in fulfilling core tasks using best-practice tools and methodologies while receiving feedback from the facilitator and your peers.



Candidates for the Azure AI Engineer Associate certification should have subject matter expertise building, managing, and deploying AI solutions that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework.



Their responsibilities include participating in all phases of AI solutions development—from requirements definition and design to development, deployment, maintenance, performance tuning, and monitoring.



Azure AI Engineers work with solution architects to translate their vision and with data scientists, data engineers, IoT specialists, and AI developers to build complete end-to-end AI solutions.



Candidates for this certification should be proficient in C# or Python and should be able to use REST-based APIs and SDKs to build computer vision, natural language processing, knowledge mining, and conversational AI solutions on Azure.



They should also understand the components that make up the Azure AI portfolio and the available data storage options. Plus, candidates need to understand and be able to apply responsible AI principle.



Plan and manage an Azure Cognitive Services solution

Implement Computer Vision solutions

Implement natural language processing solutions

Implement knowledge mining solutions

Implement conversational AI solutions



Plan and Manage an Azure Cognitive Services Solution (15-20%)

Select the appropriate Cognitive Services resource

 select the appropriate cognitive service for a vision solution

 select the appropriate cognitive service for a language analysis solution

 select the appropriate cognitive Service for a decision support solution

 select the appropriate cognitive service for a speech solution

Plan and configure security for a Cognitive Services solution

 manage Cognitive Services account keys

 manage authentication for a resource

 secure Cognitive Services by using Azure Virtual Network

 plan for a solution that meets responsible AI principles

Create a Cognitive Services resource

 create a Cognitive Services resource

 configure diagnostic logging for a Cognitive Services resource

 manage Cognitive Services costs

 monitor a cognitive service

 implement a privacy policy in Cognitive Services

Plan and implement Cognitive Services containers

 identify when to deploy to a container

 containerize Cognitive Services (including Computer Vision API, Face API, Text Analytics,
Speech, Form Recognizer)

 deploy Cognitive Services Containers in Microsoft Azure

Implement Computer Vision Solutions (20-25%)

Analyze images by using the Computer Vision API

 retrieve image descriptions and tags by using the Computer Vision API

 identify landmarks and celebrities by using the Computer Vision API

 detect brands in images by using the Computer Vision API

 moderate content in images by using the Computer Vision API

 generate thumbnails by using the Computer Vision API

Extract text from images

 extract text from images or PDFs by using the Computer Vision service

 extract information using pre-built models in Form Recognizer

 build and optimize a custom model for Form Recognizer

Extract facial information from images

 detect faces in an image by using the Face API

 recognize faces in an image by using the Face API

 analyze facial attributes by using the Face API

 match similar faces by using the Face API

Implement image classification by using the Custom Vision service

 label images by using the Computer Vision Portal

 train a custom image classification model in the Custom Vision Portal

 train a custom image classification model by using the SDK

 manage model iterations

 evaluate classification model metrics

 publish a trained iteration of a model

 export a model in an appropriate format for a specific target

 consume a classification model from a client application

 deploy image classification custom models to containers

Implement an object detection solution by using the Custom Vision service

 label images with bounding boxes by using the Computer Vision Portal

 train a custom object detection model by using the Custom Vision Portal

 train a custom object detection model by using the SDK

 manage model iterations

 evaluate object detection model metrics

 publish a trained iteration of a model

 consume an object detection model from a client application

 deploy custom object detection models to containers

Analyze video by using Video Indexer

 process a video

 extract insights from a video

 moderate content in a video

 customize the Brands model used by Video Indexer

 customize the Language model used by Video Indexer by using the Custom Speech service

 customize the Person model used by Video Indexer

 extract insights from a live stream of video data

Implement Natural Language Processing Solutions (20-25%)

Analyze text by using the Text Analytics service

 retrieve and process key phrases

 retrieve and process entity information (people, places, urls, etc.)

 retrieve and process sentiment

 detect the language used in text

Manage speech by using the Speech service

 implement text-to-speech

 customize text-to-speech

 implement speech-to-text

 Improve speech-to-text accuracy

 Improve text-to-speech accuracy

 implement intent recognition

Translate language

 translate text by using the Translator service

 translate speech-to-speech by using the Speech service

 translate speech-to-text by using the Speech service

Build an initial language model by using Language Understanding Service (LUIS)

 create intents and entities based on a schema, and then add utterances

 create complex hierarchical entities

o use this instead of roles

 train and deploy a model

Iterate on and optimize a language model by using LUIS

 implement phrase lists

 implement a model as a feature (i.e. prebuilt entities)

 manage punctuation and diacritics

 implement active learning

 monitor and correct data imbalances

 implement patterns

Manage a LUIS model

 manage collaborators

 manage versioning

 publish a model through the portal or in a container

 export a LUIS package

 deploy a LUIS package to a container

 integrate Bot Framework (LUDown) to run outside of the LUIS portal

Implement Knowledge Mining Solutions (15-20%)

Implement a Cognitive Search solution

 create data sources

 define an index

 create and run an indexer

 query an index

 configure an index to support autocomplete and autosuggest

 boost results based on relevance

 implement synonyms

Implement an enrichment pipeline

 attach a Cognitive Services account to a skillset

 select and include built-in skills for documents

 implement custom skills and include them in a skillset

Implement a knowledge store

 define file projections

 define object projections

 define table projections

 query projections

Manage a Cognitive Search solution

 provision Cognitive Search

 configure security for Cognitive Search

 configure scalability for Cognitive Search

Manage indexing

 manage re-indexing

 rebuild indexes

 schedule indexing

 monitor indexing

 implement incremental indexing

 manage concurrency

 push data to an index

 troubleshoot indexing for a pipeline

Implement Conversational AI Solutions (15-20%)

Create a knowledge base by using QnA Maker

 create a QnA Maker service

 create a knowledge base

 import a knowledge base

 train and test a knowledge base

 publish a knowledge base

 create a multi-turn conversation

 add alternate phrasing

 add chit-chat to a knowledge base

 export a knowledge base

 add active learning to a knowledge base

 manage collaborators

Design and implement conversation flow

 design conversation logic for a bot

 create and evaluate *.chat file conversations by using the Bot Framework Emulator

 choose an appropriate conversational model for a bot, including activity handlers and dialogs

Create a bot by using the Bot Framework SDK

 use the Bot Framework SDK to create a bot from a template

 implement activity handlers and dialogs

 use Turn Context

 test a bot using the Bot Framework Emulator

 deploy a bot to Azure

Create a bot by using the Bot Framework Composer

 implement dialogs

 maintain state

 implement logging for a bot conversation

 implement prompts for user input

 troubleshoot a conversational bot

 test a bot

 publish a bot

 add language generation for a response

 design and implement adaptive cards

Integrate Cognitive Services into a bot

 integrate a QnA Maker service

 integrate a LUIS service

 integrate a Speech service

 integrate Dispatch for multiple language models

 manage keys in app settings file



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Microsoft AI-102 certification

 

Microsoft companions With Labor Unions On AI training As Job Loss concerns Mount | Killexams.com Resources

Topline

Microsoft and the nation’s largest federation of labor unions launched a partnership Monday to coach laborers and leaders on how synthetic intelligence is used within the workplace—amid rising concerns from worker's and labor unions that AI could lead to job losses.

The partnership comes amid an increase in labor unions across industries searching for insurance plan from the ... [+] rising use of AI. (photograph by way of Raymond Boyd/Getty images)

Getty photographs Key data

Microsoft’s partnership with the AFL-CIO, which is made from 60 country wide and foreign labor unions representing 12.5 million people, “is the first of its form between a labor corporation and a expertise business” focused on AI, according to a Microsoft press release.

in the course of the partnership, union laborers and leaders will obtain training beginning subsequent wintry weather from experts at Microsoft on AI traits, how the know-how works and is developed and what its challenges are.

workers and union leaders will additionally deliver comments on their adventure working with AI and their considerations in regards to the know-how to Microsoft’s AI developers and researchers, hosting “labor summits” between the union and Microsoft.

The AFL-CIO and Microsoft say they are going to additionally “propose and guide policies” to instruct workers on artificial intelligence, together with tech apprenticeships—although Microsoft didn't present further details on these plans in its press unlock.

The AFL-CIO has raised concerns about AI in the past: The group commissioned a survey in August that discovered 70% of americans are worried about AI displacing workers, with girls being extra concerned about “AI’s abilities to exacerbate inequity on the job.”

Likewise, Microsoft President Brad Smith reportedly mentioned at an experience asserting the partnership he “can’t sit here and say that AI will on no account displace a job,” but that the partnership with labor unions “can support make certain that AI serves the nation’s worker's.”

besides the fact that children, tech people are less concerned about the rising technology besides the fact that they’re more uncovered to it: A Pew analysis center survey in July discovered 32% of U.S. employees in tips and expertise jobs spoke of the know-how would support greater than hurt them, in comparison to 11% who answered that it will harm greater than assist.

Key history

The AFL-CIO’s partnership with Microsoft follows a summer of union moves regarding the use of AI within the workplace. Hollywood writers and actors went on strike for months partly as a result of considerations AI can be used to put in writing scripts or substitute actors, or proficient the use of material written via writers. In an settlement to end the Writers Guild of america strike, AI can't be used to put in writing or rewrite literary cloth, and studios cannot drive writers to make use of the technology, notwithstanding writers are allowed to make use of AI in the event that they have consent from studios. A separate strike with the aid of the reveal Actors Guild-American Federation of television and Radio Artists labor union ended after an contract that included mandatory consent from actors or representatives for deceased actors for a studio to create digital replicas, and for residences to compensate performers if their digital reproduction is getting used. concerns about AI extend neatly past the enjoyment industry, although: in keeping with a Pew research look at in July, around a fifth of all laborers in the U.S. are in a job it really is exposed to AI, which means it could actually both be replaced or assisted by the technology. In can also, IBM CEO Arvind Krishna spoke of the tech gigantic would stop hiring humans to fill roles that the company believes AI can perform.

Tangent

AFL-CIO and Microsoft additionally struck a neutrality agreement for future organizing through people in AFL-CIO affiliated unions. The neutrality agreement protects the appropriate of personnel to kind or join unions and goals to create a “high-quality and cooperative” relationship between the unions and management. The framework follows the neutrality agreement last year between Microsoft and the Communications people of the united states Union involving video video game developers at Activision and ZeniMax.

further reading

Microsoft and Labor Unions form ‘historical’ Alliance on AI (Bloomberg)


 

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