Book Image

Microsoft Azure AI Fundamentals AI-900 Exam Guide

By : Aaron Guilmette, Steve Miles
Book Image

Microsoft Azure AI Fundamentals AI-900 Exam Guide

By: Aaron Guilmette, Steve Miles

Overview of this book

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.
Table of Contents (20 chapters)
Free Chapter
1
Part 1: Identify Features of Common AI Workloads
4
Part 2: Describe the Fundamental Principles of Machine Learning on Azure
8
Part 3: Describe Features of Computer Vision Workloads on Azure
11
Part 4: Describe Features of Natural Language Processing (NLP) Workloads on Azure
14
Part 5: Describe Features of Generative AI Workloads on Azure

Identify Common Machine Learning Techniques

So far, we’ve introduced you to AI technologies (such as computer vision or generative AI) as well as Microsoft’s principles for responsible AI. Now, it’s time to start talking about the substance of AI.

One of the most important questions you might have about AI is how AI manages to know what it does. Just as humans learn, AI systems have been designed to be capable of learning. And, just like humans learn through a variety of mechanisms (such as memorization and practice or repetition), AI systems also learn through different techniques and scenarios.

To be honest, though, the term machine learning is a bit of a misnomer. Since computers aren’t exactly sentient at this point, one might argue that they’re not capable of really learning. What they are capable of, however, is something quite useful: examining vast data sets to establish patterns and predict outcomes. Humans can sometimes be pretty good...