Ultimate Microsoft Certified Azure AI Engineer Associate (AI-102) Certification Guide (en Inglés) - Pablo Piovano

Ultimate Microsoft Certified Azure AI Engineer Associate (AI-102) Certification Guide

Book Description

Ultimate Microsoft Certified Azure AI Engineer Associate (AI-102) Certification Guide is a comprehensive and practical resource for professionals preparing for the AI-102 certification and building real-world expertise with Microsoft Azure AI services. It combines certification-focused guidance with hands-on engineering practices to help readers master both the exam, and the job role of an Azure AI Engineer.

The book covers all major AI-102 domains, including planning and managing Azure AI solutions, implementing generative AI, intelligent agents, computer vision, natural language processing, speech, and knowledge mining. Each chapter aligns with the latest AI-102 skills outline and reflects modern Azure AI capabilities such as Azure AI Foundry, Azure OpenAI, agent frameworks, and Retrieval-Augmented Generation (RAG), ensuring that readers learn skills that are current and industry-relevant.

What you will learn

● Understand the full AI-102 exam structure, objectives, and expectations.

● Plan, design, and manage scalable Azure AI solutions with confidence.

● Implement generative AI, RAG pipelines, and Azure OpenAI workloads.

Who is This Book For?

This book is for software developers, Azure developers, AI engineers, data engineers, solution architects, and IT professionals preparing for the Microsoft AI-102 certification or working with Azure AI solutions. Readers should have basic programming skills (C# or Python), familiarity with Azure fundamentals, and an understanding of core cloud concepts; prior AI experience is helpful but not mandatory.

Table of Contents

1. Introduction to the AI-102 Certification and Azure AI

2. Planning and Managing Azure AI Solutions

3. Implementing Generative AI Solutions.

4. Implementing Agent-Based Solutions

5. Implementing Computer Vision Solutions

6. Implementing Natural Language Processing (NLP) Solutions.

7. Implementing Knowledge Mining and Information Extraction Solutions

8. Exam Preparation Strategies

9. Practice Test and Sample Questions.

       Index