Exam AI-900: Microsoft Azure AI Fundamentals

Study guide for Exam AI-900: MicrosoftAzure 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 thisdocument should help you focus your studies as you prepare for the exam.

Useful links

Description

Review the skills measured
as of May 4, 2023

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 May 4, 2023

Study this list of skills if you take your exam PRIOR to the dateprovided.

Change log

You can go directly to the change log if you want to see thechanges that will be made on the date provided.

How to earn thecertification

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
profileConnecting 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.

1

Exam AI-900: Microsoft Azure AI Fundamentals

Useful links

Description

Take a free PracticeAssessment

Test your skills with practice questions to help you prepare for theexam.

Updates to the exam
Our exams are updated periodically to reflect skills that are required to perform a role. We haveincluded 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 otherlanguages, 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 thelocalized 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 preferredlanguage, you can request an additional 30 minutes to complete the exam.

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

NoteMost questions cover features that are general availability (GA). The exam may contain questions onPreview features if those features are commonly used.

Skills measured as of May 4, 2023
This exam is an opportunity to demonstrate knowledge of machine learning (ML) and artificial
intelligence (AI) concepts and related Microsoft Azure services. Candidates for this exam should havefamiliarity with AI-900’s self-paced or instructor-led learning material.
This exam is intended for candidates with both technical and non-technical backgrounds. Data scienceand software engineering experience are not required; however, awareness of cloud basics and clientserver applications would be beneficial.
Azure AI Fundamentals can be used to prepare for other Azure role-based certifications like Azure Data
Scientist Associate or Azure AI Engineer Associate, but it is not a prerequisite for any of them.
•Describe Artificial Intelligence workloads and considerations (20–25%)••

Describe fundamental principles of machine learning on Azure (25–30%)
Describe features of computer vision workloads on Azure (15–20%)•Describe features of Natural Language Processing (NLP) workloads on Azure (25–30%)

2

Exam AI-900: Microsoft Azure AI Fundamentals

Describe Artificial Intelligence workloads and considerations (20–25%)
Identify features of common AI workloads•

Identify features of anomaly detection workloads•

Identify computer vision workloads•

Identify natural language processing workloads•

Identify knowledge mining 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 (25–30%)
Identify common machine learning types•

Identify regression machine learning scenarios•

Identify classification machine learning scenarios•

Identify clustering machine learning scenarios

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 capabilities of visual tools in Azure Machine Learning Studio•

Automated machine learning•

Azure Machine Learning designer

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

3

Exam AI-900: Microsoft Azure AI Fundamentals

Identify Azure tools and services for computer vision tasks•

Identify capabilities of the Computer Vision service••

Identify capabilities of the Custom Vision service
Identify capabilities of the Face service•

Identify capabilities of the Form Recognizer service

Describe features of Natural Language Processing (NLP) workloads
on Azure (25–30%)
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•

Identify capabilities of the Language service
Identify capabilities of the Speech service•

Identify capabilities of the Translator service•

Identify considerations for conversational AI solutions on Azure•

Identify features and uses for bots•

Identify capabilities of the Power Virtual Agents and Azure Bot service

Study resources
We recommend that you train and get hands-on experience before you take the exam. We offer selfstudy 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

4

Exam AI-900: Microsoft Azure AI Fundamentals

Study resources

Links to learning and documentation
Natural language processing technology
Azure Bot Service
Speech to Text
Speech Translation

Ask a question

Microsoft Q&A | Microsoft DocsGet community support

Artificial Intelligence and Machine Learning HubFollow 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 ofthe exam skills measured and the third column describes the extent of the changes.

Skill area prior to May 4, 2023

Skill area as of May 4, 2023

Audience profile

Changes
Nochange

Describe Artificial Intelligenceworkloads and considerations

Describe Artificial Intelligenceworkloads and considerations

Nochange

Identify features of common AI workloads

Identify features of common AI workloads

NochangeIdentify guiding principles for responsible
AI

Identify guiding principles for responsible
AINochangeDescribe fundamental principles ofmachine learning on Azure

Describe fundamental principles ofmachine learning on Azure

No
changeIdentify common machine learning types

Identify common machine learning types

Nochange5

Exam AI-900: Microsoft Azure AI Fundamentals

Skill area prior to May 4, 2023

Skill area as of May 4, 2023

Changes

Describe core machine learning concepts

Describe core machine learning concepts

NochangeDescribe capabilities of visual tools in
Azure Machine Learning Studio

Describe capabilities of visual tools in
Azure Machine Learning StudioNochangeDescribe features of computer vision
workloads on Azure

Describe features of computer vision
workloads on Azure

Nochange

Identify common types of computer vision
solution

Identify common types of computer vision
solution

Minor

Identify Azure tools and services forcomputer vision tasks

Identify Azure tools and services forcomputer vision tasks

NochangeDescribe features of Natural Language
Processing (NLP) workloads on AzureDescribe features of Natural Language
Processing (NLP) workloads on Azure

Nochange

Identify features of common NLP workloadscenarios

Identify features of common NLP
workload scenarios

Nochange

Identify Azure tools and services for NLP
workloads

Identify Azure tools and services for NLP
workloads

NochangeIdentify considerations for conversational
AI solutions on Azure

Identify considerations for conversational
AI solutions on Azure

MinorSkills measured prior to May 4, 2023
Audience profile
This exam is an opportunity to demonstrate knowledge of machine learning (ML) and artificial
intelligence (AI) concepts and related Microsoft Azure services. Candidates for this exam should havefamiliarity with AI-900’s self-paced or instructor-led learning material.
This exam is intended for candidates with both technical and non-technical backgrounds. Data scienceand software engineering experience are not required; however, awareness of cloud basics and clientserver applications would be beneficial.
Azure AI Fundamentals can be used to prepare for other Azure role-based certifications like Azure Data
Scientist Associate or Azure AI Engineer Associate, but it is not a prerequisite for any of them.
•

Describe Artificial Intelligence workloads and considerations (20–25%)
Describe fundamental principles of machine learning on Azure (25–30%)•

Describe features of computer vision workloads on Azure (15–20%)•6

Exam AI-900: Microsoft Azure AI Fundamentals•

Describe features of Natural Language Processing (NLP) workloads on Azure (25–30%)

Describe Artificial Intelligence workloads and considerations (20–25%)
Identify features of common AI workloads•

identify features of anomaly detection workloads
identify computer vision workloads•identify natural language processing workload•

identify knowledge mining 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 fortransparency in an AI solution•

describe considerations for accountability in an AI solution

Describe fundamental principles of machine learning on Azure (25–30%)
Identify common machine learning types•

identify regression machine learning scenarios
identify classification machine learning scenarios•

identify clustering machine learning scenarios•

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 capabilities of visual tools in Azure Machine Learning Studio•

automated machine learning•

Azure Machine Learning designer

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, facial recognition, and facial analysis solutions•

7

Exam AI-900: Microsoft Azure AI Fundamentals

Identify Azure tools and services for computer vision tasks•

identify capabilities of the Computer Vision service••

identify capabilities of the Custom Vision service
identify capabilities of the Face service•

identify capabilities of the Form Recognizer service

Describe features of Natural Language Processing (NLP) workloads
on Azure (25–30%)
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•

identify capabilities of the Language service
identify capabilities of the Speech service•

identify capabilities of the Translator service•

Identify considerations for conversational AI solutions on Azure•

identify features and uses for bots•

identify capabilities of the Azure Bot service

8