Exam AI-900 Microsoft Azure AI Fundamentals

This exam is an opportunity to demonstrate knowledge of common ML and AI workloads and how to implement them on Azure. It is intended for candidates with both technical and non-technical backgrounds.

Candidates for this exam should have foundational knowledge of machine learning (ML) and artificial intelligence (AI) concepts and related Microsoft Azure services. Data science and software engineering experience are not required; however, some general programming knowledge or experience would be beneficial.

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.

• 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%)

• 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

• 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 regression machine learning scenarios

• Identify classification machine learning scenarios

• Identify clustering machine learning scenarios

• Identify features of deep learning techniques

• 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

• 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

• 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

• Identify features of generative AI models

• Identify common scenarios for generative AI

• Identify responsible AI considerations for generative AI

• 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