AI-900 Exam Study Guide (Microsoft Azure AI Fundamentals)

In this AI-900 Study Guide, I will share both free and paid options, whether books, video training, or simply links to articles and blog posts.

Watch the AI-900 Study Guide Microsoft Azure AI Fundamentals Video. 👇🏾

AI-900 Microsoft Learning Path

Don’t miss these free, self-paced online resources to help you gain the skills needed to earn your certification. AI-900 online learning paths.

AI-900 Instructor-led training (Microsoft Official Courses)

This course introduces fundamental concepts related to artificial intelligence (AI), and the services in Microsoft Azure that can be used to create AI solutions. The course is not designed to teach students to become professional data scientists or software developers, but rather to build awareness of common AI workloads and the ability to identify Azure services to support them.

The course is designed as a blended learning experience that combines instructor-led training with online materials on the Microsoft Learn platform (https://azure.com/learn). The hands-on exercises in the course are based on Learn modules, and students are encouraged to use the content on Learn as reference materials to reinforce what they learn in the class and to explore topics in more depth. $550 Course AI-900T00: Microsoft Azure AI Fundamentals

AI-900 Video Training Options

This learning path is designed to help you prepare for the AI-900 Microsoft Azure AI Fundamentals.

Tech Simplified Microsoft Azure AI Fundamentals AI-900 Training Course $50

AI-900 Practice Exams

Microsoft Official Practice Tests are self-study tools that prepare candidates for the Microsoft required exams. $99.00 - $109.00 Microsoft Official Practice Test Fundamentals - Microsoft Official Practice Test

Another practice test and sample questions. Free Examtopics.com Microsoft AI-900 Exam

Audience Profile for the Exam

The Azure AI Fundamentals course is designed for anyone interested in learning about the types of solution artificial intelligence (AI) makes possible, and the services on Microsoft Azure that you can use to create them. You don’t need to have any experience of using Microsoft Azure before taking this course, but a basic level of familiarity with computer technology and the Internet is assumed.

Some of the concepts covered in the course require a basic understanding of mathematics, such as the ability to interpret charts. The course includes hands-on activities that involve working with data and running code, so a knowledge of fundamental programming principles will be helpful.

About Exam AI-900: Microsoft Azure AI Fundamentals

This exam is intended for candidates with both technical and non-technical backgrounds. Data science and software engineering experience are not required; however, awareness of cloud basics and client-server applications would be beneficial.

Azure AI Fundamentals can be used to prepare for other Azure role-based certifications like Azure Data Scientist Associate or Azure AI Engineer Associate, but it is not a prerequisite for any of them.

Microsoft Study Guide

This exam changes quickly we do our best to stay updated but always check Microsoft exam page for updates,

Objective domains

This section itemizes the topics covered in the Exam Prep session and links to Microsoft documentation so you can review the topics in detail.

• Describe Artificial Intelligence workloads and considerations (20-25%)

• Describe fundamental principles of machine learning on Azure (25-30%)

• Describe features of computer vision workloads on Azure (15-20%)

• Describe features of Natural Language Processing (NLP) workloads on Azure (25-30%)

• Describe features of generative AI workloads on Azure (15–20%)

  
  

Describe AI workloads and considerations (15–20%)

Identify Features of Common AI Workloads

• Identify features of content moderation and personalization workloads

• Identify computer vision workloads

• Identify natural language processing workloads

• Identify knowledge mining workloads

• Identify document intelligence workloads

• Identify features of generative AI workloads

Identify guiding principles for responsible AI

• Describe considerations for fairness in an AI solution

• Describe considerations for reliability and safety in an AI solution

• Describe considerations for privacy and security in an AI solution

• Describe considerations for inclusiveness in an AI solution

• Describe considerations for transparency in an AI solution

• Describe considerations for accountability in an AI solution

Describe fundamental principles of machine learning on Azure (30–35%)

Identify common machine learning types

• Identify regression machine learning scenarios

• Identify classification machine learning scenarios

• Identify clustering machine learning scenarios

• Identify features of deep learning techniques

  
  

Describe core machine learning concepts

• Identify features and labels in a dataset for machine learning

• Describe how training and validation datasets are used in machine learning

Describe Azure Machine Learning capabilities

• Describe capabilities of automated machine learning

• Describe data and compute services for data science and machine learning

• Describe model management and deployment capabilities in Azure Machine Learning

Describe features of Computer Vision workloads on Azure (15–20%)

Identify common types of Computer Vision solution

• Identify features of image classification solutions

• Identify features of object detection solutions

• Identify features of optical character recognition solutions

• Identify features of facial detection and facial analysis solutions

  
  

Identify Azure tools and services for Computer Vision tasks

• Describe capabilities of the Azure AI Vision service

• Describe capabilities of the Azure AI Face detection service

Describe features of natural language processing (NLP) workloads on Azure (15–20%)

Identify features of common NLP workload scenarios

• Identify features and uses for key phrase extraction

• Identify features and uses for entity recognition

• Identify features and uses for sentiment analysis

• Identify features and uses for language modeling

• Identify features and uses for speech recognition and synthesis

• Identify features and uses for translation

Identify Azure tools and services for NLP workloads

• Describe capabilities of the Azure AI Language service

• Describe capabilities of the Azure AI Speech service

• Describe capabilities of the Azure AI Translator service

Describe features of generative AI workloads on Azure (15–20%)

Identify features of generative AI solutions

• Identify features of generative AI models

• Identify common scenarios for generative AI

• Identify responsible AI considerations for generative AI

  
  

Identify capabilities of Azure OpenAI Service

• Describe natural language generation capabilities of Azure OpenAI Service

• Describe code generation capabilities of Azure OpenAI Service

• Describe image generation capabilities of Azure OpenAI Service

Access Tech Simplified Microsoft Azure AI Fundamentals AI-900 Training Course