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. |
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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