CheapestExamDumps!

Microsoft Exam Syllabus

AI-900 syllabus, skills measured, and exam topics

The AI-900 exam measures Describe Artificial Intelligence workloads and considerations, Describe fundamental principles of machine learning on Azure, and Describe features of computer vision workloads on Azure. Use this page to review the current official syllabus, major domains, and source links before exam day.

Open the practice page

Skills measured by domain

Use the weighting table to decide where to spend the most study time.

Domain Weight
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%
Describe fundamental principles of machine learning on Azure 15-20%

What to know before you study

These sections explain the role, audience, and exam framing behind the outline.

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

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.

Detailed outline

Scan each section as a working study checklist instead of one long wall of text.

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

  • Identify computer vision workloads
  • Identify natural language processing workloads
  • Identify document processing workloads
  • Identify features of generative AI workloads
  • 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 (15-20%)

  • Identify regression machine learning scenarios
  • Identify classification machine learning scenarios
  • Identify clustering machine learning scenarios
  • Identify features of deep learning techniques
  • Identify features of the Transformer architecture
  • Identify features and labels in a dataset for machine learning
  • Describe how training and validation datasets are used in machine learning
  • 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 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
  • 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 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
  • Describe capabilities of the Azure AI Language service
  • Describe capabilities of the Azure AI Speech service