Microsoft Azure AI Fundamentals AI-900 Exam Guide : Gain Proficiency in Azure AI and Machine Learning Concepts and Services to Excel in the AI-900 Exam.

Format:

Book

Author/Creator:

Guilmette, Aaron.

Contributor:

Miles, Steve.

Tender, Peter De.

Language:

English

Subjects (All):

Artificial intelligence--Study guides--Examinations.

Artificial intelligence.

Artificial intelligence--Examinations--Study guides.

Microsoft Azure (Computing platform)--Study guides--Examinations.

Microsoft Azure (Computing platform).

Microsoft Azure (Computing platform)--Examinations--Study guides.

Physical Description:

1 online resource (288 pages)

Edition:

1st ed.

Place of Publication:

Birmingham B3 2PB, UK. : Packt Publishing, 2024.

Birmingham : Packt Publishing, Limited, 2024.

Biography/History:

Guilmette Aaron: Aaron Guilmette is a Principal Architect at Planet Technologies, an award-winning Microsoft Partner focused on the Public Sector. As an author of over a dozen IT books, he specializes in identity, messaging, and automation technologies. Previous to Planet Technologies, Aaron was a Senior Program Manager for Microsoft focusing on Microsoft 365 Customer Experience. When he's not writing books or tools for his customers, Aaron can be found tinkering on cars. Miles Steve: Steve Miles works in a technology leadership role for the cloud practice of a multi-billion turnover IT distributor based in the UK and Ireland. He is a Microsoft Azure MVP (Most Valuable Professional), MCT (Microsoft Certified Trainer) and Microsoft technologies author. Steve has more than 25 years of experience in hosted datacenter services, hybrid, and multi-cloud platforms. In his free time, Steve also can be found tinkering on cars.

Summary:

Get ready to pass the certification exam on your first attempt by gaining actionable insights into AI concepts, ML techniques, and Azure AI services covered in the latest AI-900 exam syllabus from two industry experts Key FeaturesDiscover Azure AI services, including computer vision, Auto ML, NLP, and OpenAIExplore AI use cases, such as image identification, chatbots, and moreWork through 145 practice questions under chapter-end self-assessments and mock examsPurchase of this book unlocks access to web-based exam prep resources, including mock exams, flashcards, and exam tipsBook DescriptionThe AI-900 exam helps you take your first step into an AI-shaped future. Regardless of your technical background, this book will help you test your understanding of the key AI-related topics and tools used to develop AI solutions in Azure cloud. This exam guide focuses on AI workloads, including natural language processing (NLP) and large language models (LLMs). You’ll explore Microsoft’s responsible AI principles like safety and accountability. Then, you’ll cover the basics of machine learning (ML), including classification and deep learning, and learn how to use training and validation datasets with Azure ML. Using Azure AI Vision, face detection, and Video Indexer services, you’ll get up to speed with computer vision-related topics like image classification, object detection, and facial detection. Later chapters cover NLP features such as key phrase extraction, sentiment analysis, and speech processing using Azure AI Language, speech, and translator services. The book also guides you through identifying GenAI models and leveraging Azure OpenAI Service for content generation. At the end of each chapter, you’ll find chapter review questions with answers, provided as an online resource. By the end of this exam guide, you’ll be able to work with AI solutions in Azure and pass the AI-900 exam using the online exam prep resources.What you will learnDiscover various types of artificial intelligence (AI)workloads and services in AzureCover Microsoft's guiding principles for responsible AI development and useUnderstand the fundamental principles of how AI and machine learning workExplore how AI models can recognize content in images and documentsGain insights into the features and use cases for natural language processingExplore the capabilities of generative AI servicesWho this book is forWhether you're a cloud engineer, software developer, an aspiring data scientist, or simply interested in learning AI/ML concepts and capabilities on Azure, this book is for you. The book also serves as a foundation for those looking to attempt more advanced AI and data science-related certification exams (e.g. Microsoft Certified: Azure AI Engineer Associate). Although no experience in data science and software engineering is required, basic knowledge of cloud concepts and client-server applications is assumed.

Contents:

Cover

Title page

Copyright and Credits

Foreword

Contributors

Table of Contents

Preface

Part 1: Identify Features of Common AI Workloads

Chapter 1: Identify Features of Common AI Workloads

Making the Most Out of this Book - Your Certification and Beyond

Identify features of data monitoring and anomaly detection workloads

Identify features of content moderation and personalization workloads

Identify computer vision workloads

Identify natural language processing workloads

Identify document intelligence workloads

Summary

Exam Readiness Drill

Working On Timing

Chapter 2: Identify the Guiding Principles for Responsible AI

Understanding ethical principles

Describe considerations for accountability

Describe considerations for inclusiveness

Describe considerations for reliability and safety

Understand explainable principles

Describe considerations for fairness

Describe considerations for transparency

Describe considerations for privacy and security

Exam Readiness Drill - Chapter Review Questions

Part 2: Describe the Fundamental Principles of Machine Learning on Azure

Chapter 3: Identify Common Machine Learning Techniques

Understanding machine learning terminology

Training

Inferencing

Identify regression machine learning scenarios

Example

Evaluation metrics

Applications

Identify classification machine learning scenarios

Binary classification

Multiclass classification

Identify clustering machine learning scenarios

Identify features of deep learning techniques

Working On Timing.

Chapter 4: Describe Core Machine Learning Concepts

Identify features and labels in a dataset for machine learning

Identifying features in a dataset

Identifying labels in a dataset

Describe how training and validation datasets are used in machine learning

Training set

Validation set

Chapter 5: Describe Azure Machine Learning Capabilities

What is Azure ML?

Describe capabilities of AutoML

AutoML use cases

Training, validation, and test scenarios

Feature engineering

Ensemble models

Describe data and compute services for data science and machine learning

Compute

Data

Datastore

Environments

Model

Workspaces

Subscription

Storage account

Key Vault

Application Insights

Container Registry

Describe model management and deployment capabilities in Azure ML

Model management and deployment capabilities

MLOps

Build a machine learning model in Azure ML

Creating a machine learning workspace

Using AutoML to train a model

Reviewing and selecting the best model

Deploying and testing the model

Testing the deployed model service

Teardown

Part 3: Describe Features of Computer Vision Workloads on Azure

Chapter 6: Identify Common Types of Computer Vision Solutions

Introduction to CV solutions

Image processing

CV ML

Identify features of image classification solutions

Identify features of object detection solutions

Identify features of OCR solutions

Identify features of facial detection and facial analysis solutions

Facial detection

Facial analysis

Facial recognition

Exam Readiness Drill - Chapter Review Questions.

Exam Readiness Drill

Chapter 7: Identify Azure Tools and Services for Computer Vision Tasks

Technical requirements

Describe capabilities of the Azure AI Vision service

Image classification

Object detection

OCR solutions

Describe the capabilities of the Azure AI Face service

Getting started

Responsible AI

Describe capabilities of the Azure AI Video Indexer service

Part 4: Describe Features of Natural Language Processing (NLP) Workloads on Azure

Chapter 8: Identify Features of Common NLP Workload Scenarios

Introduction to NLP

NLP concepts

NLP scenarios

Identify features and uses for key phrase extraction

Identify features and uses for entity recognition

Identify features and uses for sentiment analysis

Identify features and uses for language modeling

Conversational language understanding (CLU)

Conversational AI

Identify features and uses for speech recognition and synthesis

Speech recognition

Speech synthesis

Identify features and uses for translation

Chapter 9: Identify Azure Tools and Services for NLP Workloads

Describe capabilities of the Azure AI Language service

Text analysis

Conversational language understanding

Question-answering

Azure AI Language Studio

Describe capabilities of the Azure AI Speech service

Azure AI Speech Studio

Describe capabilities of the Azure AI Translator service

Part 5: Describe Features of Generative AI Workloads on Azure.

Chapter 10: Identify Features of Generative AI Solutions

What is Generative AI?

Identify Features of Generative AI models

What's a transformer model and how does it work?

How does generative AI put all this together?

Identify common scenarios for generative AI

Image generation

Text generation

Music creation

Synthetic data generation

Code generation

Voice generation and transformation

Drug discovery and chemical synthesis

Personalized content and recommendation systems

Maintenance analysis

Copilots

Deepfake creation and detection

Quality control

Identify Responsible AI considerations for generative AI

Identify

Measure

Mitigate

Operate

Chapter 11: Identify Capabilities of Azure OpenAI Service

What is Azure OpenAI Service?

What's included?

What's the difference between Azure AI and Azure OpenAI services?

Accessing Azure OpenAI services

Describe natural language generation capabilities of Azure OpenAI Service

Describe code generation capabilities of Azure OpenAI Service

Describe image generation capabilities of Azure OpenAI Service

Chapter 12: Accessing the Online Practice Resources

How to Access These Resources

Purchased from Packt Store (packtpub.com)

Packt+ Subscription

Purchased from Amazon and Other Sources

Troubleshooting Tips

Practice Resources - A Quick Tour

A Clean, Simple Cert Practice Experience

Practice Questions

Flashcards

Exam Tips

Chapter Review Questions

Share Feedback

Back to the Book

Index

Other Books You May Enjoy.

Notes:

Title from eBook information screen..

Description based on publisher supplied metadata and other sources.

ISBN:

9781835885673

1835885675

OCLC:

1435752040