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Study guide for Exam AI-900: Microsoft Azure AI Fundamentals

Purpose of this document

This study guide should help you understand what to expect on the exam and includes a summary of the topics the exam might cover and links to additional resources. The information and materials in this document should help you focus your studies as you prepare for the exam.

Useful links Description
Review the skills measured as of April 24, 2024 This list represents the skills measured AFTER the date provided. Study this list if you plan to take the exam AFTER that date.
Review the skills measured prior to April 24, 2024 Study this list of skills if you take your exam PRIOR to the date provided.
Change log You can go directly to the change log if you want to see the changes that will be made on the date provided.
How to earn the certification Some certifications only require passing one exam, while others require passing multiple exams.
Certification renewal Microsoft associate, expert, and specialty certifications expire annually. You can renew by passing a free online assessment on Microsoft Learn.
Your Microsoft Learn profile Connecting your certification profile to Microsoft Learn allows you to schedule and renew exams and share and print certificates.
Exam scoring and score reports A score of 700 or greater is required to pass.
Exam sandbox You can explore the exam environment by visiting our exam sandbox.
Request accommodations If you use assistive devices, require extra time, or need modification to any part of the exam experience, you can request an accommodation.
Take a free Practice Assessment Test your skills with practice questions to help you prepare for the exam.

Updates to the exam

Our exams are updated periodically to reflect skills that are required to perform a role. We have included two versions of the Skills Measured objectives depending on when you are taking the exam.

We always update the English language version of the exam first. Some exams are localized into other languages, and those are updated approximately eight weeks after the English version is updated. While Microsoft makes every effort to update localized versions as noted, there may be times when the localized versions of an exam are not updated on this schedule. Other available languages are listed in the Schedule Exam section of the Exam Details webpage. If the exam isn't available in your preferred language, you can request an additional 30 minutes to complete the exam.

Note

The bullets that follow each of the skills measured are intended to illustrate how we are assessing that skill. Related topics may be covered in the exam.

Note

Most questions cover features that are general availability (GA). The exam may contain questions on Preview features if those features are commonly used.

Skills measured as of April 24, 2024

Audience profile

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.

Skills at a glance

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

  • Describe fundamental principles of machine learning on Azure (20–25%)

  • 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 (15–20%)

Describe Artificial Intelligence 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 (20–25%)

Identify common machine learning techniques

  • 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 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

Study resources

We recommend that you train and get hands-on experience before you take the exam. We offer self-study options and classroom training as well as links to documentation, community sites, and videos.

Study resources Links to learning and documentation
Get trained Choose from self-paced learning paths and modules or take an instructor-led course
Find documentation Anomaly Detector
Language Understanding
Azure Machine Learning
Computer Vision
Natural language processing technology
Azure Bot Service
Speech to Text
Speech Translation
Ask a question Microsoft Q&A | Microsoft Docs
Get community support Artificial Intelligence and Machine Learning Hub
Follow Microsoft Learn Microsoft Learn - Microsoft Tech Community
Find a video The AI Show
Browse other Microsoft Learn shows

Change log

Key to understanding the table: The topic groups (also known as functional groups) are in bold typeface followed by the objectives within each group. The table is a comparison between the two versions of the exam skills measured and the third column describes the extent of the changes.

Skill area prior to April 24, 2024 Skill area as of April 24, 2024 Change
Audience profile No change
Describe Artificial Intelligence workloads and considerations Describe Artificial Intelligence workloads and considerations No change
Identify features of common AI workloads Identify features of common AI workloads No change
Identify guiding principles for responsible AI Identify guiding principles for responsible AI No change
Describe fundamental principles of machine learning on Azure Describe fundamental principles of machine learning on Azure No change
Identify common machine learning techniques Identify common machine learning techniques No change
Describe core machine learning concepts Describe core machine learning concepts No change
Describe Azure Machine Learning capabilities Describe Azure Machine Learning capabilities Minor
Describe features of computer vision workloads on Azure Describe features of computer vision workloads on Azure No change
Identify common types of computer vision solution Identify common types of computer vision solution No change
Identify Azure tools and services for computer vision tasks Identify Azure tools and services for computer vision tasks Minor
Describe features of Natural Language Processing (NLP) workloads on Azure Describe features of Natural Language Processing (NLP) workloads on Azure No change
Identify features of common NLP Workload Scenarios Identify features of common NLP Workload Scenarios No change
Identify Azure tools and services for NLP workloads Identify Azure tools and services for NLP workloads Minor
Describe features of generative AI workloads on Azure Describe features of generative AI workloads on Azure No change
Identify features of generative AI solutions Identify features of generative AI solutions No change
Identify capabilities of Azure OpenAI Service Identify capabilities of Azure OpenAI Service No change

Skills measured prior to April 24, 2024

Audience profile

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.

Skills at a glance

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

  • Describe fundamental principles of machine learning on Azure (20–25%)

  • 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 (15–20%)

Describe Artificial Intelligence 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 (20–25%)

Identify common machine learning techniques

  • 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 capabilities of the Azure AI Video Indexer 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