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Prepare for Microsoft Exam AI-900 and help demonstrate your real-world knowledge of diverse machine learning (ML) and artificial intelligence (AI) workloads, and how they can be implemented with Azure AI. Designed for business stakeholders, new and existing IT professionals, consultants, and students, this Exam Ref focuses on the critical thinking and decision-making acumen needed for success at the Microsoft Certified: Azure AI Fundamentals level.

Focus on the expertise measured by these objectives:

Describe AI workloads and considerations

Describe fundamental principles of machine learning on Azure

Describe features of computer vision workloads on Azure

Describe features of Natural Language Processing (NLP) workloads on Azure

Describe features of conversational AI workloads on Azure

This Microsoft Exam Ref:

Organizes its coverage by exam objectives

Features strategic, what-if scenarios to challenge you

Assumes you are a business user, stakeholder, technical professional, or student who wants to become familiar with Azure AI; requires no data science or software engineering experience.

About the Exam

Exam AI-900 focuses on knowledge needed to identify features of common AI workloads and guiding principles for responsible AI; identify common ML types; describe core ML concepts; identify core tasks in creating an ML solution; describe capabilities of no-code ML with Azure Machine Learning Studio; identify common types of computer vision solutions; identify Azure tools and services for computer vision tasks; identify features of common NLP workload scenarios; identify Azure tools and services for NLP workloads; and identify common use cases and Azure services for conversational Al.

About Microsoft Certification

Passing this exam fulfills your requirements for the Microsoft Certified: Azure AI Fundamentals certification, demonstrating your knowledge of common ML and AI workloads and how to implement them on Azure. With this certification, you can move on to earn more advanced role-based certifications, including Microsoft Certified: Azure AI Engineer Associate or Azure Data Scientist Associate.

See full details at: microsoft.com/learn

Table of Contents

  1. Cover Page
  2. About This eBook
  3. Title Page
  4. Copyright Page
  5. Pearson’s Commitment to Diversity, Equity, and Inclusion
  6. Dedications
  7. Contents at a glance
  8. Contents
  9. Acknowledgments
  10. About the author
  11. Introduction
    1. Organization of this book
    2. Preparing for the exam
    3. Microsoft certifications
    4. Quick access to online references
    5. Errata, updates, & book support
    6. Stay in touch
  12. Chapter 1 Describe Artificial Intelligence workloads and considerations
    1. Skill 1.1: Identify features of common AI workloads
    2. Describe Azure services for AI and ML
    3. Understand Azure Machine Learning
    4. Understand Azure Cognitive Services
    5. Describe the Azure Bot Service
    6. Identify common AI workloads
    7. Skill 1.2: Identify guiding principles for Responsible AI
    8. Describe the Fairness principle
    9. Describe the Reliability & Safety principle
    10. Describe the Privacy & Security principle
    11. Describe the Inclusiveness principle
    12. Describe the Transparency principle
    13. Describe the Accountability principle
    14. Understand Responsible AI for Bots
    15. Understand Microsoft’s AI for Good program
    16. Chapter summary
    17. Thought experiment
    18. Thought experiment answers
  13. Chapter 2 Describe fundamental principles of machine learning on Azure
    1. Skill 2.1: Identify common machine learning types
    2. Understand machine learning model types
    3. Describe regression models
    4. Describe classification models
    5. Describe clustering models
    6. Skill 2.2: Describe core machine learning concepts
    7. Understand the machine learning workflow
    8. Identify the features and labels in a dataset for machine learning
    9. Describe how training and validation datasets are used in machine learning
    10. Describe how machine learning algorithms are used for model training
    11. Select and interpret model evaluation metrics
    12. Skill 2.3: Identify core tasks in creating a machine learning solution
    13. Understand machine learning on Azure
    14. Understand Azure Machine Learning studio
    15. Describe data ingestion and preparation
    16. Describe feature selection and engineering
    17. Describe model training and evaluation
    18. Describe model deployment and management
    19. Skill 2.4: Describe capabilities of no-code machine learning with Azure Machine Learning
    20. Describe Azure Automated Machine Learning
    21. Describe Azure Machine Learning designer
    22. Chapter summary
    23. Thought experiment
    24. Thought experiment answers
  14. Chapter 3 Describe features of computer vision workloads on Azure
    1. Skill 3.1: Identify common types of computer vision solution
    2. Introduce Cognitive Services
    3. Understand computer vision
    4. Describe image classification
    5. Describe object detection
    6. Describe optical character recognition
    7. Describe facial detection, recognition, and analysis
    8. Skill 3.2: Identify Azure tools and services for computer vision tasks
    9. Understand the capabilities of the Computer Vision service
    10. Understand the Custom Vision service
    11. Understand the Face service
    12. Understand the Form Recognizer service
    13. Chapter summary
    14. Thought experiment
    15. Thought experiment answers
  15. Chapter 4 Describe features of Natural Language Processing (NLP) workloads on Azure
    1. Skill 4.1: Identify features of common NLP workload scenarios
    2. Describe Natural Language Processing
    3. Describe language modeling
    4. Describe key phrase extraction
    5. Describe named entity recognition
    6. Describe sentiment analysis
    7. Describe speech recognition and synthesis
    8. Describe translation
    9. Skill 4.2: Identify Azure tools and services for NLP workloads
    10. Identify the capabilities of the Text Analytics service
    11. Identify the capabilities of the Language Understanding service (LUIS)
    12. Identify the capabilities of the Speech service
    13. Identify the capabilities of the Translator service
    14. Chapter summary
    15. Thought experiment
    16. Thought experiment answers
  16. Chapter 5 Describe features of conversational AI workloads on Azure
    1. Skill 5.1: Identify common use cases for conversational AI
    2. Identify features and uses for webchat bots
    3. Identify common characteristics of conversational AI solutions
    4. Skill 5.2: Identify Azure services for conversational AI
    5. Identify capabilities of the QnA Maker service
    6. Identify capabilities of the Azure Bot Service
    7. Chapter summary
    8. Thought experiment
    9. Thought experiment answers
  17. Index
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