Practice Exams | Microsoft Azure AI-102 | Azure AI Solution

Overview

Be prepared for the Microsoft Azure Exam AI-102: Designing and Implementing an Azure AI Solution | Azure AI Engineer

What you'll learn:

Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution
Select the appropriate Cognitive Services resource
Plan and configure security for a Cognitive Services solution
Create a Cognitive Services resource
Plan and implement Cognitive Services containers
Analyze images by using the Computer Vision API
Extract text from images
Extract facial information from images
Implement image classification by using the Custom Vision service
Implement an object detection solution by using the Custom Vision service
Analyze video by using Video Indexer
Analyze text by using the Text Analytics service
Manage speech by using the Speech service
Translate language
Build an initial language model by using Language Understanding Service (LUIS)
Iterate on and optimize a language model by using LUIS
Manage a LUIS model
Implement a Cognitive Search solution
Implement an enrichment pipeline
Implement a knowledge store
Manage a Cognitive Search solution
Manage indexing
Create a knowledge base by using QnA Maker
Design and implement conversation flow
Create a bot by using the Bot Framework SDK
Create a bot by using the Bot Framework Composer
Integrate Cognitive Services into a bot

In order to set realistic expectations, please note: These questions are NOT official questions that you will find on the official exam. These questions DO cover all the material outlined in the knowledge sections below. Many of the questions are based on fictitious scenarios which have questions posed within them.

The official knowledge requirements for the exam are reviewed routinely to ensure that the content has the latest requirements incorporated in the practice questions. Updates to content are often made without prior notification and are subject to change at any time.

Each question has a detailed explanation and links to reference materials to support the answers which ensures accuracy of the problem solutions.

The questions will be shuffled each time you repeat the tests so you will need to know why an answer is correct, not just that the correct answer was item "B" last time you went through the test.

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#, Python, or JavaScript and should be able to use REST-based 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 on Microsoft Azure AI-102 Exam

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 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
configure persons and person groups
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

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
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
add language generation for a response
design and implement adaptive cards

Create a bot by using the Bot Framework SDK