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Apr 25, 2024 · Learn how to prepare for the exam that tests your knowledge of AI concepts and Azure services. Find out the skills measured, exam details, practice resources, and certification benefits.
- Microsoft Azure AI Fundamentals: AI Overview - Training
Artificial Intelligence (AI) empowers amazing new solutions...
- Study guide for Exam AI-900: Microsoft Azure AI Fundamentals
You can use Azure AI Fundamentals to prepare for other Azure...
- AI-900: Microsoft Azure AI Fundamentals Study Guide
This comprehensive study guide provides a thorough overview...
- Master the basics with the Azure AI Fundamentals certification
The Azure AI Fundamentals certification validates your...
- Microsoft Azure AI Fundamentals: AI Overview - Training
Learn the basics of artificial intelligence, machine learning, and Azure AI services in this 3-hour learning path. Explore fundamental concepts, types, challenges, and applications of AI with hands-on exercises and quizzes.
- Updates to the exam
- Skills measured as of January 31, 2024
- Change log
- Skills measured prior to January 31, 2024
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.
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
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 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
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.
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
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 data monitoring and anomaly detection 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 Identify features of common AI workloads •Identify features of data monitoring and anomaly detection 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
Learn the core concepts and skills of artificial intelligence and the services on Microsoft Azure to create AI solutions. Prepare for the AI-900 certification exam with five courses, interactive exercises, and a discount voucher.
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Feb 9, 2023 · Learn how to prepare for the AI-900 exam that measures your knowledge of AI and ML concepts and Azure services. Find resources, topics, and tips from Microsoft Learn, instructor-led course, documentation, and practice assessment.
The Azure AI Fundamentals exam measures your understanding of AI fundamentals, Azure AI services, machine learning models, data preparation and processing, and best practices for AI.
Sep 17, 2020 · Learn how to earn your certification in Azure AI Fundamentals and master the basics of machine learning and AI concepts and services. Find out the prerequisites, training options, exam details, and benefits of this certification.
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