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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
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?
