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Microsoft AI-102 Exam Syllabus

Microsoft AI-102 Exam

Designing and Implementing a Microsoft Azure AI Solution

Total Questions: 267

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Microsoft AI-102 Exam Overview :

Exam Name Designing and Implementing a Microsoft Azure AI Solution
Exam Code AI-102
Official Information https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
See Expected Questions Microsoft AI-102 Expected Questions in Actual Exam
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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

Updates in the Microsoft AI-102 Exam Syllabus:

Microsoft AI-102 exam questions and practice test are the best ways to get fully prepared. Study4exam's trusted preparation material consists of both practice questions and practice test. To pass the actual Azure AI Engineer Associate AI-102 exam on the first attempt, you need to put in hard work on these Microsoft AI-102 questions that provide updated information about the entire exam syllabus. Besides studying actual questions, you should take the Microsoft AI-102 practice test for self-assessment and actual exam simulation. Revise actual exam questions and remove your mistakes with the Designing and Implementing a Microsoft Azure AI Solution AI-102 exam practice test. Online and windows-based formats of the AI-102 exam practice test are available for self-assessment.