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

Understand explainable principles

The concept of explainability in AI is crucial for data scientists, auditors, and business decision makers. It enables these stakeholders to understand and justify the decisions made by AI systems and the reasoning behind them. In terms of Microsoft’s responsible AI principles, explainability covers three principles:

  • Fairness, or the ability for the system to make decisions that don’t discriminate or apply a bias toward groups or individuals based on identifiers such as gender, race, religion, or sexual orientation
  • Transparency in understanding how a model arrived at its result
  • Securing the data inputs and outputs to protect the privacy of both organization and personal data

Explainability is vital for ensuring compliance with company policies, industry standards, and government regulations. For data scientists, it involves being able to explain how they achieved specific levels of accuracy and what factors influenced the outcome. Auditors...