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Microsoft Azure AI Fundamentals AI-900 Exam Guide

Microsoft Azure AI Fundamentals AI-900 Exam Guide

By : Aaron Guilmette, Steve Miles
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Microsoft Azure AI Fundamentals AI-900 Exam Guide

Microsoft Azure AI Fundamentals AI-900 Exam Guide

4 (1)
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)
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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 Azure Tools and Services for NLP Workloads

In Chapter 8, Identify Features of Common NLP Workload Scenarios, you learned how to identify various Natural Language Processing (NLP) capabilities provided by Microsoft AI services, such as key phrase extraction, entity recognition, sentiment analysis, language modeling, speech recognition, and translation, all of which are common NLP workload scenarios for Azure.

This chapter builds on the foundational concepts of NLP presented in Chapter 8, Identify features of Common NLP Workload Scenarios, and links the features and capabilities to AI services in the Azure platform.

The objectives and skills we’ll cover in this chapter are as follows:

  • Describe capabilities of the Azure AI Language service
  • Describe capabilities of the Azure AI Speech service
  • Describe capabilities of the Azure AI Translator service
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Microsoft Azure AI Fundamentals AI-900 Exam Guide
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