Azure AI-102 Certification Essentials : Master the AI Engineer Associate Exam with Real-World Case Studies and Full-length Mock Tests.

Format:

Book

Author/Creator:

Lee, Peter T.

Language:

English

Subjects (All):

Cloud computing--Examination--Study guides.

Cloud computing.

Microsoft Azure (Computing platform).

Physical Description:

1 online resource (388 pages)

Edition:

1st ed.

Place of Publication:

Birmingham : Packt Publishing, Limited, 2025.

Summary:

Azure AI-102 Certification Essentials is your comprehensive guide to mastering Azure's AI tools and earning the AI-102 certification. Through practical examples, hands-on exercises, and expert guidance, this book will help you understand and effectively use Azure AI technologies to design and integrate AI solutions. You'll also prepare for the exam with realistic mock questions and detailed explanations. What this Book will help me do Understand AI basics, concepts, and Azure's AI service capabilities to design AI solutions effectively. Implement Azure AI technologies such as Vision, Language, and Search services, gaining hands-on expertise. Apply responsible AI principles to ensure fairness, reliability, and security in AI deployments. Prepare and refine your skills using comprehensive mock exams and practical guidance for the AI-102 certification. Leverage Azure tools to design, build, and deploy AI models that meet modern enterprise needs. Author(s) Peter T. Lee is a solutions architect and Microsoft AI professional with more than 25 years of experience in IT. With a deep passion for artificial intelligence and a talent for simplifying complex concepts, Peter has helped many professionals grow their skills and succeed in certification exams. His dedication to responsible AI is evident in the thoughtfulness and depth of his writings. Who is it for? This book is ideal for developers, engineers, and professionals transitioning to AI-centric roles who want to master Azure AI technologies. Even students and educators implementing AI/ML concepts will find this resource invaluable. Whether enhancing your career or preparing for the Azure AI-102 certification, this book equips you with the understanding and skills needed to succeed.

Contents:

Intro

Title Page

Copyright and Credits

Dedication

Contributors

Table of Contents

Preface

Part 1: Foundations and Essentials of Azure AI

Chapter 1: Understanding AI, ML, and Azure's AI Services

Foundations of AI: exploring ML, LMs, and key AI capabilities

AI

ML

Deep learning

Six key AI capabilities

Exploring Azure AI services

Azure AI Search

Document Intelligence

Video Indexer

Azure OpenAI Service

Azure Vision

Azure Speech

Azure Language

Content Safety

Summary

Review questions

Further reading

Chapter 2: Getting Started with Azure AI: Studios, Pipelines, and Containerization

Technical requirements

Various AI studios

Creating and configuring Azure AI services

Exercise 1: Getting started with Azure AI services

Integrating CI/CD in Azure AI and machine learning development

Traditional versus AI-based system testing and monitoring

Key considerations for CI/CD in AI and machine learning projects

Container deployment strategies

Exercise 2: Using an Azure AI services container

Chapter 3: Managing, Monitoring, and Securing Azure AI Services

Managing diagnostic logging

Exercise 1: Creating resources for diagnostic log storage

Exercise 2: Viewing log data in Azure Log Analytics

Monitoring metrics

Add metric

Adding a metric to a dashboard

Managing costs for Azure AI services

Planning costs

Viewing costs

Setting up cost alerts

Exercise 3: Setting up an alert rule

Exercise 4: Visualizing a metric

Understanding authentication

Exercise 5: Regenerating keys

Protecting keys with Azure Key Vault

Microsoft Entra ID authentication

Authenticating requests to Azure AI services

Exercise 6: Managing Azure AI services security.

Configuring network security

Managing default network access rules

Granting access from a virtual network

Part 2: Practical Applications of Azure AI

Chapter 4: Implementing Content Moderation Solutions

Planning for responsible AI principles

Recognizing the risks associated with generative AI

Innovating responsibly through iteration

Understanding built-in security and safety systems

Implementing mitigating strategies

Leveraging Azure AI Content Safety

Azure AI Content Safety overview

Content safety evaluation in Azure AI Foundry

Exercise 1: Content filtering via Azure OpenAI

Exercise 2: Create an Azure AI Content Safety resource

Exercise 3: Image content via AI Foundry

Chapter 5: Exploring Azure AI Vision Solutions

Analyzing images

Exercise 1: Analyzing images using Azure AI Vision

Implementing model customization

Custom model types overview

Creating a custom project

Labeling and training a custom model

Exercise 2: Creating a custom model training project

Implementing the Azure AI Face service

Key features of Azure AI Face

Common use cases for Azure AI Face

Getting started with the Azure AI Face service

Exercise 3: Detecting and analyzing faces using the Azure AI Face service (Python)

Overview of OCR in Azure AI Vision

How OCR works in Azure AI Vision

Common use cases for OCR

Exercise 4: Reading text in images using Azure AI Vision OCR (Python)

Analyzing videos with Azure AI Video Indexer

Key features of video analysis in Azure AI Vision

Common use cases for video analysis

Getting started with video analysis in Azure AI Vision

Exercise 5: Analyzing video content using Azure AI Video Indexer (Python)

Review questions.

Further reading

Chapter 6: Implementing Natural Language Processing Solutions

Analyzing text by using Azure AI Language

Exercise 1: Text analysis with Azure AI Language

Processing speech by using Azure AI Speech

Key features

Accessing the Azure AI Speech service

Configuring audio formats and voices

Exercise 2: Recognizing and synthesizing speech

Translating text/speech with speech services

Translating speech to text using the SDK

Synthesize translations (speech-to-speech translation)

Exercise 3: Translating documents from a source language to a target language

Exercise 4: Translating speech using Azure AI Speech

Building a conversational language understanding model

Exercise 5: Building a conversational language understanding model

Creating a custom question-answering solution by using Azure AI Language

Exercise 6: Creating question-answering solution

Developing NLP solutions

Exercise 7: Creating custom text classification

Chapter 7: Implementing Knowledge Mining, Document Intelligence, and Content Understanding

Exploring Azure AI Search

Azure AI Search process

Exercise 1: Creating an Azure AI Search service

Understanding indexes, skillsets, and indexers in the Azure portal

Managing knowledge store projections

Exercise 2: Creating an index, skillset, indexer, custom skill, and knowledge store within VS Code

Implementing the Document Intelligence solution

Document Intelligence capabilities

Exercise 3: Document Intelligence Studio/Azure AI Foundry - UI interface and no coding

Exercise 4: Document Intelligence client libraries approach

Understanding Azure AI Content Understanding

What is Azure AI Content Understanding?

Exercise 5: Analyzing content with Azure AI Content Understanding

Summary.

Review questions

Chapter 8: Working on Generative AI Solutions

Azure AI Foundry

Overview of Azure AI Foundry

Exercise 1: Creating a hub, project, and AI service in the Azure portal

Using Azure OpenAI to generate content

Exercise 2: Deploying Azure OpenAI

Advanced techniques in generative AI: DALL-E 3, the RAG pattern, prompt engineering, and fine-tuning

Exercise 3: Using DALL-E 3 to generate images

Exercise 4: Applying prompt engineering techniques

Exercise 5: The RAG pattern (using your own data)

Exercise 6: Fine-tuning models with your own data

Part 3: Agentic AI Solutions, Applying Real-World Use Cases, and Preparing for the AI-102 Certification

Chapter 9: Implementing Agentic Solutions with Azure AI Agent Service

Understanding AI agents and their use cases

Configuring resources to build an agent

Testing, optimizing, and deploying agents

Chapter 10: Practical AI Implementation: Industry Use Cases, Technical Patterns, and Hands-On Projects

Industry use cases and key technical patterns

Modern AI tools in enterprise

AI across industries

Learning accelerators projects on GitHub

Chat your own data

The RAG pattern with database: using function calling to access and query structured data

AI Search

Chapter 11: Preparing for the AI-102 Azure AI Engineer Associate Certification Exam

Strategies and tips for success

Master key concepts through explanation

Hands-on practice

Thoroughly practice and analyze test questions

Prioritize high-weighted topics first

Exam tips

Practice exams

Index

About Packt

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Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

1-83620-526-0

OCLC:

1528955553

Publisher Number:

CIPO000250652