Microsoft Certified: Azure AI Engineer Associate

Prepare to pass the Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution certification exam to be awarded the Microsoft Certified: Azure AI Engineer Associate.

Course Level: Intermediate

Role: AI Engineer

Duration: 4 Days

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Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution

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 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.

Course Outline

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

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

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