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 scenarios for generative AI

Generative AI, with its ability to create new content, has applications spanning numerous fields. Here are some common scenarios where generative AI is making a significant impact, along with examples for each.

Image generation

Multimodal generative AI can create new images from textual descriptions, such as generating photorealistic images of objects or scenes that don’t exist, using models such as OpenAI’s DALL-E or Midjourney:

Figure 10.2 – Generating an image with GPT4

Figure 10.2 – Generating an image with GPT4

Text generation

Generative AI can be used to produce coherent and contextually relevant text for articles or stories by utilizing models such as GPT-3 from OpenAI:

Figure 10.3 – Text generation using GPT-3.5

Figure 10.3 – Text generation using GPT-3.5

Music creation

Generative AI can compose new music pieces or songs in various genres by learning from a vast dataset of music, exemplified by projects such as OpenAI...