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
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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 ...
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. Table of Contents - Identify Features of Common AI Workloads - Identify the Guiding Principles for Responsible AI - Identify Common Machine Learning Techniques - Describe Core Machine Learning Concepts - Describe Azure Machine Learning Capabilities - Identify Common Types of Computer Vision Solutions - Identify Azure Tools and Services for Computer Vision Tasks - Identify Features of Common NLP Workload Scenarios - Identify Azure Tools and Services for NLP Workloads - Identify Features of Generative AI Solutions - Identify Capabilities of Azure OpenAI Service
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