Exam Ref AI-900 Microsoft Azure AI Fundamentals

Exam Ref AI-900 Microsoft Azure AI Fundamentals

by Julian Sharp
Released December 2021
Publisher(s): Microsoft Press
ISBN: 9780137358076

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Book description

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
      1. Describe Azure services for AI and ML
      2. Understand Azure Machine Learning
      3. Understand Azure Cognitive Services
      4. Describe the Azure Bot Service
      5. Identify common AI workloads
    2. Skill 1.2: Identify guiding principles for Responsible AI
      1. Describe the Fairness principle
      2. Describe the Reliability & Safety principle
      3. Describe the Privacy & Security principle
      4. Describe the Inclusiveness principle
      5. Describe the Transparency principle
      6. Describe the Accountability principle
      7. Understand Responsible AI for Bots
      8. Understand Microsoft’s AI for Good program
    3. Chapter summary
    4. Thought experiment
    5. Thought experiment answers
  13. Chapter 2 Describe fundamental principles of machine learning on Azure
    1. Skill 2.1: Identify common machine learning types
      1. Understand machine learning model types
      2. Describe regression models
      3. Describe classification models
      4. Describe clustering models
    2. Skill 2.2: Describe core machine learning concepts
      1. Understand the machine learning workflow
      2. Identify the features and labels in a dataset for machine learning
      3. Describe how training and validation datasets are used in machine learning
      4. Describe how machine learning algorithms are used for model training
      5. Select and interpret model evaluation metrics
    3. Skill 2.3: Identify core tasks in creating a machine learning solution
      1. Understand machine learning on Azure
      2. Understand Azure Machine Learning studio
      3. Describe data ingestion and preparation
      4. Describe feature selection and engineering
      5. Describe model training and evaluation
      6. Describe model deployment and management
    4. Skill 2.4: Describe capabilities of no-code machine learning with Azure Machine Learning
      1. Describe Azure Automated Machine Learning
      2. Describe Azure Machine Learning designer
    5. Chapter summary
    6. Thought experiment
    7. 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
      1. Introduce Cognitive Services
      2. Understand computer vision
      3. Describe image classification
      4. Describe object detection
      5. Describe optical character recognition
      6. Describe facial detection, recognition, and analysis
    2. Skill 3.2: Identify Azure tools and services for computer vision tasks
      1. Understand the capabilities of the Computer Vision service
      2. Understand the Custom Vision service
      3. Understand the Face service
      4. Understand the Form Recognizer service
    3. Chapter summary
    4. Thought experiment
    5. 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
      1. Describe Natural Language Processing
      2. Describe language modeling
      3. Describe key phrase extraction
      4. Describe named entity recognition
      5. Describe sentiment analysis
      6. Describe speech recognition and synthesis
      7. Describe translation
    2. Skill 4.2: Identify Azure tools and services for NLP workloads
      1. Identify the capabilities of the Text Analytics service
      2. Identify the capabilities of the Language Understanding service (LUIS)
      3. Identify the capabilities of the Speech service
      4. Identify the capabilities of the Translator service
    3. Chapter summary
    4. Thought experiment
    5. 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
      1. Identify features and uses for webchat bots
      2. Identify common characteristics of conversational AI solutions
    2. Skill 5.2: Identify Azure services for conversational AI
      1. Identify capabilities of the QnA Maker service
      2. Identify capabilities of the Azure Bot Service
    3. Chapter summary
    4. Thought experiment
    5. Thought experiment answers
  17. Index

Product information

  • Title: Exam Ref AI-900 Microsoft Azure AI Fundamentals
  • Author(s): Julian Sharp
  • Release date: December 2021
  • Publisher(s): Microsoft Press
  • ISBN: 9780137358076