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

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 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 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. ]]> 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 Summary Exam Readiness Drill – Chapter Review Questions Exam Readiness Drill 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 Example Evaluation metrics Applications Identify features of deep learning techniques Example Applications Summary Exam Readiness Drill – Chapter Review Questions 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 Summary Exam Readiness Drill – Chapter Review Questions Exam Readiness Drill Working On Timing 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 Summary Exam Readiness Drill – Chapter Review Questions Exam Readiness Drill Working On Timing 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 Summary Exam Readiness Drill – Chapter Review Questions Exam Readiness Drill Working On Timing 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 Facial detection Responsible AI Describe capabilities of the Azure AI Video Indexer service Summary Exam Readiness Drill – Chapter Review Questions Exam Readiness Drill Working On Timing 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 Summary Exam Readiness Drill – Chapter Review Questions Exam Readiness Drill Working On Timing Chapter 9: Identify Azure Tools and Services for NLP Workloads Technical requirements 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 Summary Exam Readiness Drill – Chapter Review Questions Exam Readiness Drill Working On Timing 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 Summary Exam Readiness Drill – Chapter Review Questions Exam Readiness Drill Working On Timing 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 Summary Exam Readiness Drill – Chapter Review Questions Exam Readiness Drill Working On Timing 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