AI-102 Exam Study Guide (Designing and Implementing a Microsoft Azure AI Solution)

AI-102 Exam Study Guide (Designing And Implementing A Microsoft Azure AI Solution)

AI-102 Preparation Details

Preparing for AI-102 Designing and Implementing an Azure AI Solution Certificate exam? Don’t know where to start? This post is the AI-102 Certificate Study Guide (with links to each exam objective).

I have curated a list of articles from Microsoft documentation for each objective of the AI-102 exam. I hope this article will help you to prepare for the AI-102 Certification exam. Also, please share the post within your circles so it helps them to prepare for the exam.

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AI-102 Design an Azure AI Solution Course

Udemy MS Azure AI Solution Complete Exam Prep
Pluralsight Microsoft Azure AI Engineer Certification
LinkedIn Learning (Free trial) Microsoft Cognitive Services for Developers

AI-102 Azure AI Solution Practice Test and Lab

Whizlabs Exam Questions Azure AI Practice Questions on cert. exam
Udemy Practice Tests New AI Test: Design & Implement Azure AI

AI-102 Azure AI Solution Study Materials

Coursera Deep Learning Specialization by Andrew Ng
Amazon e-book (PDF) Learning Microsoft Cognitive Services
Udacity (Nanodegree) Become an Azure Machine Learning Engineer

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Confused between AI-102 and DP-100? You are not alone. Read the AI-102 vs DP-100 blog post and choose the one that’s right for you!

AI-102 Sample Practice Exam Questions

AI-102 Designing and Implementing an Azure AI Solution Practice Test

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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

Choosing a Microsoft cognitive services technology

Azure Cognitive Services Vision APIs

Select the appropriate cognitive service for a language analysis solution

Azure Cognitive Services Language APIs

Select the appropriate cognitive Service for a decision support solution

Azure Cognitive Services Decision APIs

Select the appropriate cognitive service for a speech solution

Azure Cognitive Services Speech APIs

Speech service

Plan and Configure Security for a Cognitive Services Solution

Manage Cognitive Services account keys

Get the keys for your resource

CLI command to manage Azure Cognitive Services accounts

What’s new? A single key for Cognitive Services

Manage authentication for a resource

Authenticate requests to Azure Cognitive Services

Secure Cognitive Services by using Azure Virtual Network

Configure Azure Cognitive Services virtual networks

Plan for a solution that meets responsible AI principles

Responsible AI principles from Microsoft

Build powerful & responsible AI solutions with Azure

Create a Cognitive Services Resource

Create a Cognitive Services resource

Create a Cognitive Services resource using the Azure portal

Create a Cognitive Services resource using the Azure CLI

Configure diagnostic logging for a Cognitive Services resource

Enable diagnostic logging for Azure Cognitive Services

Manage Cognitive Services costs

Plan and manage costs for Azure Cognitive Services

Monitor a cognitive service

Monitor operations and activity of Azure Cognitive Search

Implement a privacy policy in Cognitive Services

Data & privacy for Spatial Analysis

Azure certification Frequently Asked Questions

Plan and Implement Cognitive Services Containers

Identify when to deploy to a container

Azure Cognitive Services containers

Azure Cognitive Services containers (FAQ)

Containerize Cognitive Services (including Computer Vision API, Face API, Text Analytics, Speech, Form Recognizer)

Install Read OCR Docker containers

Face containers (Install & run)

Install & run Text Analytics containers

Run and install Docker containers for the Speech service APIs

Install and run Form Recognizer containers

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

Describe images with a human-readable language

Applying content tags to images

Identify landmarks and celebrities by using the Computer Vision API

Detect domain-specific content

Detect brands in images by using the Computer Vision API

Detect popular brands in images

Moderate content in images by using the Computer Vision API

Detect adult content

Generate thumbnails by using the Computer Vision API

Generate smart-cropped thumbnails with Computer Vision

Extract Text from Images

Extract text from images by using the OCR API

OCR API

Optical Character Recognition (OCR)

Extract text from images or PDFs by using the Read API

Read API

Convert handwritten text by using Ink Recognizer

Recognize digital ink with the Ink Recognizer REST API

Extract information from forms or receipts by using the pre-built receipt model in Form Recognizer

Form Recognizer prebuilt receipt model

Build and optimize a custom model for Form Recognizer

Build a training data set for a custom model

Train a custom model

Manage custom models

Extract Facial Information from Images

Detect faces in an image by using the Face API

Get face detection data

Recognize faces in an image by using the Face API

Quickstart: Use the Face client library

Analyze facial attributes by using the Face API

Facial attributes

Get started with Face analysis on Azure

Analyze faces with the Face service

Match similar faces by using the Face API

Face – Find Similar

AI-102 Exam Details and Tips

Implement Image Classification by Using the Custom Vision Service

Label images by using the Computer Vision Portal

Label images faster with Smart Labeler

Train a custom image classification model in the Custom Vision Portal

Build a classifier with the Custom Vision website

Train a custom image classification model by using the SDK

Create an image classification project with the Custom Vision client library

Manage model iterations

Manage training iterations

Use your model with the prediction API

Evaluate classification model metrics

Evaluate the classifier

Publish a trained iteration of a model

Publish your trained iteration

Export a model in an appropriate format for a specific target

Export your model for use with mobile devices

Consume a classification model from a client application

Consume an AML model deployed as a web service

Deploy image classification custom models to containers

Perform image classification with Custom Vision Service

Implement an Object Detection Solution by Using the Custom Vision Service

Label images with bounding boxes by using the Computer Vision Portal

Tag images & specify bounding boxes for object detection

Train a custom object detection model by using the Custom Vision Portal

Build an object detector with the Custom Vision website

Train a custom object detection model by using the SDK

Create an object detection project with the Custom Vision library

Manage model iterations

Manage training iterations

Evaluate object detection model metrics

Evaluate the detector

Publish a trained iteration of a model

Publish the current iteration

Consume an object detection model from a client application

Use the object detection model in Power Automate

Deploy custom object detection models to containers

Azure Cognitive Services containers

Analyze Video by Using Video Indexer

Process a video

Upload and index your videos

Extract insights from a video

Video Indexer – Unlock insights from your video

Moderate content in a video

Video moderation with Content Moderator

Customize the Brands model used by Video Indexer

Customize a Brands model with the Video Indexer website

Customize the Language model used by Video Indexer by using the Custom Speech Service

Customize a Language model with the Video Indexer website

Customize the Person model used by Video Indexer

Customize a Person model with the Video Indexer website

Extract insights from a live stream of video data

Live stream analysis with Video Indexer

Use Video Indexer to process a live stream & display data

Implement Natural Language Processing Solutions (20-25%)

Analyze Text by Using the Text Analytics Service

Retrieve and process key phrases

How to extract key phrases using Text Analytics?

Retrieve and process entity information (people, places, URLs, etc.)

Supported entity categories Text Analytics

How to use Named Entity Recognition in Text Analytics?

Retrieve and process sentiment

Sentiment analysis on streaming data using Databricks

Analyze sentiment & synthesize speech

Detect the language used in text

Detect language with Text Analytics

Manage Speech by Using the Speech Service

Implement text-to-speech

What is text-to-speech?

Get started with text-to-speech

Customize text-to-speech

Get started with Custom Voice

Create a Custom Voice

Implement speech-to-text

What is speech-to-text?

Get started with speech-to-text

Improve speech-to-text accuracy

Improve accuracy with tenant models

Translate Language

Translate text by using the Translator service

Create a translation app with WPF

Translate speech-to-speech by using the Speech service

Get started with speech translation

Translate speech-to-text by using the Speech service

Get started with speech-to-text

Build an Initial Language Model by Using Language Understanding Service (LUIS)

Create intents and entities based on a schema, and then add utterances

Intents in your LUIS app

Add intents to determine user intention of utterances

Extract data with entities

Add entities to extract data

Understand what good utterances are for your LUIS app

Create complex hierarchical entities

Using Hierarchical Entities in Microsoft’s LUIS for NLP

Hierarchical Entities in LUIS

Use this instead of roles

Add contributors to your app

Train and deploy a model

Train your active version of the LUIS app

Deploy an app in the LUIS portal

Iterate on and Optimize a Language Model by Using LUIS

Implement phrase lists

Create a phrase list for a concept

Add phrase list as a feature

Using Phrase Lists in Microsoft’s LUIS for NLP

Implement a model as a feature (i.e. prebuilt entities)

Model as a feature

A model as a feature helps another model

Effective prebuilt entities

Add a prebuilt entity

Prebuilt entities

Manage punctuation and diacritics

Punctuation normalization

What are Diacritics?

Diacritics normalization

Implement active learning

Concepts for enabling active learning

Log user queries to enable active learning

Monitor and correct data imbalances

Review data imbalance

Evaluate the performance of your LUIS app

Implement patterns

Patterns improve prediction accuracy

Add patterns to improve predictions

Manage a LUIS Model

Manage collaborators

Collaborate with others – LUIS

How do I give collaborators access to LUIS?

Manage versioning

Manage versions

Application and version settings

Publish a model through the portal or in a container

Publish your app to a staging or production endpoint

Install and run Docker containers for LUIS

Export a LUIS package

Export packaged app from LUIS

Export your customer data in (LUIS) in Cognitive Services

Export a version

Deploy a LUIS package to a container

Deploy LUIS on Azure Container instances

Deploy LUIS service as a Docker image

Integrate Bot Framework (LUDown) to run outside of the LUIS portal

LUDown

Creating a LUIS Service with LUDown and the CLI

ai-102 Azure Machine Learning Exam Prep Questions

Amazon link (affiliate)

Implement Knowledge Mining Solutions (15-20%)

Implement a Cognitive Search Solution

Create data sources

Create Data Source (Cognitive Search REST API)

Define an index

Create a basic search index in Azure Cognitive Search

Create and run an indexer

Create a search indexer

Run the indexer

Query an index

Querying in Azure Cognitive Search

Configure an index to support autocomplete and autosuggest

Add autocomplete to client apps using Azure Cognitive Search

Create a suggester to enable autocomplete

Boost results based on relevance

Boost search rank using scoring profiles

Implement synonyms

Synonyms in Azure Cognitive Search

Implement an Enrichment Pipeline

Attach a Cognitive Services account to a skillset

Attach a Cognitive Services resource to a skillset

Select and include built-in skills for documents

Built-in cognitive skills for text & image processing

Document Extraction cognitive skill

Implement custom skills and include them in a skillset

Add a custom skill to an Azure Cognitive Search enrichment pipeline

Implement a Knowledge Store

Define file projections

Projecting to file

Define object projections

Projecting to objects

Define table projections

Projecting to tables

Query projections

Knowledge store “projections” in Azure Cognitive Search

Manage a Cognitive Search Solution

Provision Cognitive Search

Create an Azure Cognitive Search service

Configure security for Cognitive Search

Security in Azure Cognitive Search

Configure keys for data encryption in Azure Cognitive Search

Configure IP firewall for Azure Cognitive Search

Configure scalability for Cognitive Search

Scale for performance on Azure Cognitive Search

Manage Indexing

Manage re-indexing

Update index

Rebuild indexes

Rebuild an index in Azure Cognitive Search

Schedule indexing

Schedule indexers in Azure Cognitive Search

Monitor indexing

Monitor Azure Cognitive Search indexer status

Implement incremental indexing

Incremental enrichment and caching

Manage concurrency

Manage concurrency in Azure Cognitive Search

Push data to an index

Pushing data to an index

Troubleshoot indexing for a pipeline

Troubleshooting common indexer issues

Implement Conversational AI Solutions (15-20%)

Create a Knowledge Base by Using QnA Maker

Create a QnA Maker service

Create a new QnA Maker service

Create a knowledge base

Create your QnA Maker knowledge base

Import a knowledge base

Migrate a knowledge base from QnA Maker (Step 6)

Train and test a knowledge base

Train the knowledge base

How to test a knowledge base?

Publish a knowledge base

Publish the knowledge base

Create a multi-turn conversation

Use follow-up prompts to create multiple turns of a conversation

Add alternate phrasing

Add alternate questions

Add additional alternatively-phrased questions

Get suggested alternate questions

Add chit-chat to a knowledge base

Add Chit-chat to a knowledge base

Export a knowledge base

Migrate a knowledge base using export-import

Add active learning to a knowledge base

Use active learning to improve your knowledge base

Manage collaborators

Collaborate with other authors and editors

Design and Implement Conversation Flow

Design conversation logic for a bot

Design and control conversation flow

How to design a conversation for a chatbot?

Create and evaluate *.chat file conversations by using the Bot Framework Emulator

Debug your bot using transcript files

Add language generation for a response

Language generation

Use language generation templates in your bot

Design and implement adaptive cards

Adaptive Cards Designer SDK

Designing Adaptive Cards for your Microsoft Teams app

Create a Bot by Using the Bot Framework SDK

Implement dialogs

Dialogs library

Use dialogs within a skill

Maintain state

Managing state

Implement logging for a bot conversation

Add trace activities to your bot

Implement a prompt for user input

Create your own prompts to gather user input

Add and review bot telemetry

Add telemetry to your bot

Analyze your bot’s telemetry data

Implement a bot-to-human handoff

Transition conversations from bot to human

Bot to Human Handoff in Node.js

Troubleshoot a conversational bot

Troubleshoot general

Add a custom middleware for processing user messages

Middleware

Manage identity and authentication

Bot Framework authentication basics

Add authentication to a bot

Identity providers

Implement channel-specific logic

Implement channel-specific functionality

Channel-specific functionality with the Bot Connector API

Publish a bot

Deploy a basic bot

Create a Bot by Using the Bot Framework Composer

Implement dialogs

Dialogs in Bot Framework Composer

Maintain state

Conversation flow and memory

Implement logging for a bot conversation

Conversation logging with the Composer

Implement prompts for user input

Ask for user input

Troubleshoot a conversational bot

Unable to publish my bot built with Bot Framework Composer

Test a bot by using the Bot Framework Emulator

Debug with the Emulator

Publish a bot

Publish your bot to Azure

Integrate Cognitive Services into a Bot

Integrate a QnA Maker service

Use QnA Maker to answer questions

Add a QnA Maker knowledge base to your bot

Integrate a LUIS service

Add natural language understanding to your bot

Integrate a Speech service

Add speech to messages with the Bot Connector API

Voice-enable your bot using the Speech SDK

Integrate Dispatch for multiple language models

Use multiple LUIS and QnA models

Manage keys in the app settings file

Update the settings file

This brings us to the end of the AI-102 Study Guide.

What do you think? Let me know in the comments section if I have missed out on anything. Also, I love to hear from you about how your preparation is going on!

In case you are preparing for other Azure certification exams, check out the Azure study guide for those exams.

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