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AI-102

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Microsoft Exam AI-102 Questions Answers Test Simulator

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AI-102 Exam Topics

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
  • manageauthentication 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)

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
  • identifylandmarks 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 by using the OCR API
  • extract text from images or PDFs by using the Read API
  • convert handwritten text by using Ink Recognizer
  • extract information from forms or receipts by using the pre-built receipt model in FormRecognizer
  • build andoptimize 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 byusing 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 customobject 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 Speechservice
  • customizethe 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
  • retrieveand 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

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 UnderstandingService (LUIS)

  • create intents and entities based on a schema, and then add utterances
  • create complex hierarchical entitiesouse 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 throughthe 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 datasources
  • 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 askillset
  • 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
  • manageconcurrency
  • 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
  • add language generation for a response
  • design and implement adaptive cards

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