Azure AI Fundamentals (AI-900) Study Guide

Chapter 3. Overview of AI Workloads and Key Use Cases

In this chapter, we’ll look at AI workloads and the key factors you need to know for implementing them responsibly. These workloads, which make up about 15%–20% of the AI-900 exam, are about understanding how AI works in different areas like content moderation, personalization, computer vision, NLP, knowledge mining, and document intelligence. These are the building blocks that power AI applications. They help systems analyze images, understand human language, extract insights from huge amounts of data, and create content using generative AI.

But it’s not just about knowing what these AI workloads can do. We’ll also look at the ethical and practical considerations that are crucial when developing responsible AI. We’ll talk about key principles like accountability, inclusiveness, reliability, safety, fairness, transparency, security, and privacy. These principles are essential for ensuring that AI is not only powerful but also ethical, trustworthy, and aligned with the values that matter to society.

Introduction to AI

AI is about helping computers think and respond like humans. Imagine it this way: AI lets computers make decisions, solve problems, understand language, recognize images, and create new things, just like a person would. The secret sauce behind AI is how it learns from data—a lot of data—to recognize patterns and make predictions. With AI, businesses can automate tasks, uncover insights buried in mountains of information, ...