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 features of facial detection and facial analysis solutions

CV aspects using Azure Machine Learning algorithms can be utilized as a solution for creating image and video facial detection, analysis, and recognition capabilities in applications.

Face detection and analysis capabilities in ML can be used in the areas of identity verification and security. The capabilities can provide security access controls by determining the level of access by identifying and verifying the person’s face. You could think of this like role-based access control (RBAC), but instead of controlling access based on a user’s role, the level of access is determined by their face.

Use case: This may be useful in the scenario where you wish to tag recognized friends in social media photographs; for identifying and targeting demographic groups for use in advertising; identifying celebrities; locating missing or wanted persons using CCTV footage; for intelligent monitoring of face pose...