AI-102 Microsoft Azure AI Solution Practice Tests Exam Prep

Exam AI-102: Designing and Implementing a Microsoft Azure

AI Solution

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 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 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 Orchestrator for multiple language models

 manage keys in app settings file