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--Examinations--Study guides.

Artificial intelligence.

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

Microsoft Azure (Computing platform).

Physical Description:

1 online resource (288 p.)

Place of Publication:

Birmingham : Packt Publishing, Limited, 2024.

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 Features Discover Azure AI services, including computer vision, Auto ML, NLP, and OpenAI Explore AI use cases, such as image identification, chatbots, and more Work through 145 practice questions under chapter-end self-assessments and mock exams Purchase of this book unlocks access to web-based exam prep resources, including mock exams, flashcards, and exam tips Book Description The 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 learn Discover various types of artificial intelligence (AI)workloads and services in Azure Cover Microsoft's guiding principles for responsible AI development and use Understand the fundamental principles of how AI and machine learning work Explore how AI models can recognize content in images and documents Gain insights into the features and use cases for natural language processing Explore the capabilities of generative AI services Who this book is for Whether 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

Working On Timing

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

Summary

Exam Readiness Drill

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

Notes:

Description based upon print version of record.

Using AutoML to train a model

OCLC-licensed vendor bibliographic record.

ISBN:

9781835885666

1835885667

OCLC:

1435752040