Candidates for Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution build,
manage, and deploy 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.
They 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 exam should be proficient in C# or Python and should be able to use RESTbased 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 principles.
Skills Measured
NOTE: The bullets that follow each of the skills measured are intended to illustrate how we are
assessing that skill. This list is not definitive or exhaustive.
NOTE: Most questions cover features that are general availability (GA). The exam may contain
questions on Preview features if those features are commonly used.
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
The exam guide below shows the changes that will be implemented on September 24,
2021.
Audience Profile
Candidates for Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution build,
manage, and deploy 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.
They 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 exam should be proficient in C# or Python and should be able to use RESTbased 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 principles.
Skills Measured
NOTE: The bullets that follow each of the skills measured are intended to illustrate how we are
assessing that skill. This list is not definitive or exhaustive.
NOTE: Most questions cover features that are general availability (GA). The exam may contain
questions on Preview features if those features are commonly used.
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 IndexerAzure 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 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

AI-102 exam Dumps Update - 2022-08-18

You are developing the chatbot. You create the following components: * A QnA Maker resource * A chatbot by using the Azure Bot Framework SDK. You need to integrate the components to meet the chatbot requirements. Which property should you use? A. QnADialogResponseOptions.CardNoMatchText B. Qna MakerOptions-ScoreThreshold C. Qna Maker Op t ions StrickFilters D. QnaMakerOptions.RankerType do you know this answer?