Welcome to the one-stop hub with information for preparing for the AI-900: Microsoft Azure AI Fundamentals certification exam. The content for this exam helps you to “Demonstrate fundamental AI concepts related to the development of software and services of Microsoft Azure to create AI solutions”. Upon successful completion of the exam, you earn the Microsoft Certified: Azure AI Fundamentals certification.

This hub provides information directly here (topic-by-topic as outlined in the official study guide), links to a number of external resources, tips for preparing for the exam, practice tests, and section questions to help you prepare. Bookmark this page and use it as a guide to ensure that you are fully covering all relevant topics for the AI-900 exam and making use of as many of the resources available as possible.

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Audience profile (from Microsoft’s site)

This exam is an opportunity for you to demonstrate knowledge of machine learning and AI concepts and related Microsoft Azure services. As a candidate for this exam, you should have familiarity with Exam AI-900’s self-paced or instructor-led learning material.

This exam is intended for you if you have both technical and non-technical backgrounds. Data science and software engineering experience are not required. However, you would benefit from having awareness of:
- Basic cloud concepts
- Client-server applications

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

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Skills measured at a glance (as specified in the official study guide)

• Describe Artificial Intelligence workloads and considerations (15–20%)
• Describe fundamental principles of machine learning on Azure (15–20%)
• Describe features of computer vision workloads on Azure (15–20%)
• Describe features of Natural Language Processing (NLP) workloads on Azure (15–20%)
• Describe features of generative AI workloads on Azure (20–25%)

Click on each hyperlinked topic below to go to the preparation content and practice questions for that topic. Also, there are 2 practice exams provided below.

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Describe Artificial Intelligence workloads and considerations (15–20%)

Identify features of common AI workloads

• Identify computer vision workloads
  • Practice Questions: Identify computer vision workloads
• Identify natural language processing workloads
  • Practice Questions: Identify natural language processing workloads
• Identify document processing workloads
  • Practice Questions: Identify document processing workloads
• Identify features of generative AI workloads
  • Practice Questions: Identify features of generative AI workloads

Identify guiding principles for responsible AI

• Describe considerations for fairness in an AI solution
  • Practice Questions: Describe considerations for fairness in an AI solution
• Describe considerations for reliability and safety in an AI solution
  • Practice Questions: Describe considerations for reliability and safety in an AI solution
• Describe considerations for privacy and security in an AI solution
  • Practice Questions: Describe considerations for privacy and security in an AI solution
• Describe considerations for inclusiveness in an AI solution
  • Practice Questions: Describe considerations for inclusiveness in an AI solution
• Describe considerations for transparency in an AI solution
  • Practice Questions: Describe considerations for transparency in an AI solution
• Describe considerations for accountability in an AI solution
  • Practice Questions: Describe considerations for accountability in an AI solution
• Bonus Material: Microsoft Responsible AI Principles Matrix and Scenario-to-Principle map

Describe fundamental principles of machine learning on Azure (15-20%)

Identify common machine learning techniques

• Identify regression machine learning scenarios
  • Practice Questions: Identify regression machine learning scenarios
• Identify classification machine learning scenarios
  • Practice Questions: Identify classification machine learning scenarios
• Identify clustering machine learning scenarios
  • Practice Questions: Identify clustering machine learning scenarios
• Identify features of deep learning techniques
  • Practice Questions: Identify features of deep learning techniques
• Identify features of the Transformer architecture
  • Practice Questions: Identify features of the Transformer architecture
• Bonus Material: Regression vs Classification vs Clustering

Describe core machine learning concepts

• Identify features and labels in a dataset for machine learning
  • Practice Questions: Identify features and labels in a dataset for machine learning
• Describe how training and validation datasets are used in machine learning
  • Practice Question: Describe how training and validation datasets are used in machine learning

Describe Azure Machine Learning capabilities

• Describe capabilities of automated machine learning
  • Practice Questions: Describe capabilities of automated machine learning
• Describe data and compute services for data science and machine learning
  • Practice Questions: Describe data and compute services for data science and machine learning
• Describe model management and deployment capabilities in Azure Machine Learning
  • Practice Questions: 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
  • Practice Questions: Identify features of image classification solutions
• Identify features of object detection solutions
  • Practice Questions: Identify features of object detection solutions
• Identify features of optical character recognition solutions
  • Practice Questions: Identify features of optical character recognition solutions
• Identify features of facial detection and facial analysis solutions
  • Practice Questions: 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
  • Practice Questions: Describe capabilities of the Azure AI Vision service
• Describe capabilities of the Azure AI Face detection service
  • Practice Questions: 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
  • Practice Questions: Identify features and uses for key phrase extraction
• Identify features and uses for entity recognition
  • Practice Questions: Identify features and uses for entity recognition
• Identify features and uses for sentiment analysis
  • Practice Questions: Identify features and uses for sentiment analysis
• Identify features and uses for language modeling
  • Practice Questions: Identify features and uses for language modeling
• Identify features and uses for speech recognition and synthesis
  • Practice Questions: Identify features and uses for speech recognition and synthesis
• Identify features and uses for translation
  • Practice Questions: Identify features and uses for translation

Identify Azure tools and services for NLP workloads

• Describe capabilities of the Azure AI Language service
  • Practice Questions: Describe capabilities of the Azure AI Language service
• Describe capabilities of the Azure AI Speech service
  • Practice Questions: Describe capabilities of the Azure AI Speech service

Describe features of generative AI workloads on Azure (20–25%)

Identify features of generative AI solutions

• Identify features of generative AI models
  • Practice Questions: Identify features of generative AI models
• Identify common scenarios for generative AI
  • Practice Questions: Identify common scenarios for generative AI
• Identify responsible AI considerations for generative AI
  • Practice Questions: Identify responsible AI considerations for generative AI
• Bonus Material: Generative AI vs Predictive ML vs Traditional AI

Identify generative AI services and capabilities in Microsoft Azure

• Describe features and capabilities of Azure AI Foundry
  • Practice Questions: Describe features and capabilities of Azure AI Foundry
• Describe features and capabilities of Azure OpenAI service
  • Practice Questions: Describe features and capabilities of Azure OpenAI service
• Describe features and capabilities of Azure AI Foundry model catalog
  • Practice Questions: Describe features and capabilities of Azure AI Foundry model catalog
• Bonus Material: Workload Scenarios to Correct AI Approach mappings

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AI-900 Practice Exams

We have provided 2 practice exams (with answer keys) to help you prepare:

AI-900 Practice Exam 1 (60 questions with answers)

AI-900 Practice Exam 2 (60 questions with answers)

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Important AI-900 Resources

• Link to the free, comprehensive, self-paced course on Microsoft Learn – Introduction to AI in Azure
• Link to the certification page: Microsoft Certified: Azure AI Fundamentals certification page
• Link to the study guide: Study guide for Exam AI-900: Microsoft Azure AI Fundamentals

• YouTube videos you will find useful:
  • Microsoft Learn YouTube video series for AI-900 Azure AI Fundamentals exam
  • AI-900 Azure AI Fundamentals series by Skilltech Club
  • AI-900 – AZURE AI FUNDAMENTALS EXAM (Complete Prep Series) by Learnville
  • 100 AI-900 Azure AI Fundamentals Practice Questions with Answers & Explanations | Full Mock Exam by AI & Cloud Skills Hub (this a “question and answer” video, so watch this after you have gone through the learning content, and are ready to check your preparedness)
• Books & Courses
  • There are a few books and paid courses available specifically for this exam, but we think you can learn and understand the content, prepare for the exam, and pass the exam, with the free online resources available.

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To Do’s:

• Schedule time to learn, study, perform labs, and do practice exams and questions
• Schedule the exam based on when you think you will be ready; scheduling the exam gives you a target and drives you to keep working on it; but keep in mind that it can be rescheduled based on the rules of the provider.
• Use the various resources above to learn and prepare.
• Take the free Microsoft Learn practice test, any other available practice tests, and do the practice questions in each section and the two practice tests available on this exam prep hub.

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Good luck to you passing the AI-900: Microsoft Azure AI Fundamentals certification exam and earning the Microsoft Certified: Azure AI Fundamentals certification!