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Microsoft AI-102 Exam Topics

Microsoft AI-102 Exam Overview :

Exam Name: Designing and Implementing a Microsoft Azure AI Solution
Exam Code: AI-102
Certifications: Microsoft Azure AI Engineer Associate Certification
See Expected Questions: Microsoft AI-102 Expected Questions in Actual Exam

Microsoft AI-102 Exam Topics :

Section Weight Objectives
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)
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 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 Form Recognizer
  • 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 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 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 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
  • 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 Understanding Service (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 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
  • add language generation for a response
  • design and implement adaptive cards
Create a bot by using the Bot Framework SDK
  • implement dialogs
  • maintain state
  • implement logging for a bot conversation
  • implement a prompt for user input
  • add and review bot telemetry
  • implement a bot-to-human handoff
  • troubleshoot a conversational bot
  • add a custom middleware for processing user messages
  • manage identity and authentication
  • implement channel-specific logic
  • publish a bot
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 by using the Bot Framework Emulator
  • publish a bot

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
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 Services resource
  • 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, Face API, Language, 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
  • Match similar faces by using the Face API
Implement image classification by using the Custom Vision service
  • Label images by using the Custom 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 Custom 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 Azure Video Analyzer for Media (formerly 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 Language 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
  • Create intents and entities based on a schema, and add utterances
  • Create complex hierarchical entities
  • Train and deploy a model
Iterate on and optimize a language model by using language understanding
  • 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 language understanding model
  • Manage collaborators
  • Manage versioning
  • Publish a model through the portal or in a container
  • Export a Language Service package
  • Deploy a Language Service package to a container
Create a Questions Answering solution using the Language service
  • Create a question answering project
  • Import questions and answers
  • 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
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 AI 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% Design and implement conversation flow
  • Design conversational 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 a 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 question answering model
  • Integrate a language understanding service
  • Integrate a Speech service resource
Official Information https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102

Updates in the Microsoft AI-102 Exam Topics:

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