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)

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

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AI-102 Sample Practice Exam Questions

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Plan and Manage an Azure Cognitive Services Solution (15-20%)

Select the Appropriate Cognitive Services Resource

Plan and Configure Security for a Cognitive Services Solution

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

Plan and Implement Cognitive Services 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

Configure persons and person groups

Person Group – Create

Create a new person in a person group

Add faces to a PersonGroup

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

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

Manage Speech by Using the Speech Service

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)

Iterate on and Optimize a Language Model by Using LUIS

Manage a LUIS Model

ai-102 Azure Machine Learning Exam Prep Questions

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Implement Knowledge Mining Solutions (15-20%)

Implement a Cognitive Search Solution

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

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

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

This brings us to the end of 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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