AI-900 Exam Study Guide (Microsoft Azure AI Fundamentals)

In this AI-900 Study Guide, I will share both free and paid options, whether books, video training, or simply links to articles and blog posts.

Watch the AI-900 Study Guide Microsoft Azure AI Fundamentals Video. 👇🏾

AI-900 Microsoft Learning Path

Don’t miss these free, self-paced online resources to help you gain the skills needed to earn your certification. AI-900 online learning paths.

AI-900 Instructor-led training (Microsoft Official Courses)

This course introduces fundamental concepts related to artificial intelligence (AI), and the services in Microsoft Azure that can be used to create AI solutions. The course is not designed to teach students to become professional data scientists or software developers, but rather to build awareness of common AI workloads and the ability to identify Azure services to support them.

The course is designed as a blended learning experience that combines instructor-led training with online materials on the Microsoft Learn platform (https://azure.com/learn). The hands-on exercises in the course are based on Learn modules, and students are encouraged to use the content on Learn as reference materials to reinforce what they learn in the class and to explore topics in more depth. $550 Course AI-900T00: Microsoft Azure AI Fundamentals

AI-900 Video Training

This learning path is designed to help you prepare for the AI-900 Microsoft Azure AI Fundamentals. $35 monthly subscription A Cloud Guru AI-900: Microsoft Certified Azure AI Fundamentals

AI-900 Practice Exams

Microsoft Official Practice Tests are self-study tools that prepare candidates for the Microsoft required exams. $99.00 - $109.00 Microsoft Official Practice Test Fundamentals - Microsoft Official Practice Test

Another practice test and sample questions. Free Examtopics.com Microsoft AI-900 Exam

Audience Profile for the Exam

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 have familiarity with AI-900’s self-paced or instructor-led learning material.

About Exam AI-900: Microsoft Azure AI Fundamentals

This exam is intended for candidates with both technical and non-technical backgrounds. Data science and software engineering experience are not required; however, awareness of cloud basics and client-server 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.

Skills Measured

For the full list of the skills that the exam measures, along with the level of experience and expertise that you’ll need as an exam candidate, check out the Skills measured document.

Objective domains

This section itemizes the topics covered in the Exam Prep session and links to Microsoft documentation so you can review the topics in detail.

• 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%)

Describe AI workloads and considerations (15–20%)

Identify Features of Common AI Workloads

• Overview of AI

• Machine learning

• Anomaly detection

• Computer Vision

• Natural language processing (NLP)

• Conversational AI

• AI in Microsoft azure

Identify guiding principles for responsible AI

• Principles of responsible AI

Describe fundamental principles of machine learning on Azure (30–35%)

Identify common machine learning types

• Overview of machine learning

• Regression

• Classification

• Clustering

Describe core machine learning concepts

• Features and labels in a dataset

• Training and validation dataset

• Machine learning algorithms for model training

Identify core tasks in creating a machine learning solution

• Data ingestion and preparation

• Feature engineering and selection

• Model training and evaluation

Describe capabilities of no-code machine learning with Azure Machine Learnin

• Overview of Azure Machine Learning

• Automated machine learning

• Azure Machine Learning designer

Describe features of Computer Vision workloads on Azure (15–20%)

Identify common types of Computer Vision solution

• Overview of Computer Vision

• Image classification

• Object detection

• Semantic segmentation

• Image analysis

• Optical character recognition (OCR)

• Facial detection, recognition, and

• analysis

Identify Azure tools and services for Computer Vision tasks

• ComputerVision services inAzure

• Computer Vision service

• Custom Vision service

• Face service

• Form Recognizer service

Describe features of natural language processing (NLP) workloads on Azure (15–20%)

Identify features of common NLP workload scenarios

• Overview of NLP

Identify Azure tools and services for NLP workloads

• NLP inAzure

• Text Analytics service

• Translate text and speech

• ·LanguageUnderstanding service

Describe features of conversational AI workloads on Azure (15–20%)

Identify common use cases for conversational AI

• Overview of conversational AI

• Web chatbot

• Telephone voice menu

• Personal digital assistant

Identify Azure services for conversational AI

• QnA Maker service

• Azure Bot Service