Overview
Microsoft Azure AI Fundamentals AI-900
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.
Study guide
Exam Roadmap
- Describe Artificial Intelligence workloads and considerations
- dentify 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 fundamental principles of machine learning on Azure
- 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
- Identify common machine learning techniques
- Describe core machine learning concepts
- Describe Azure Machine Learning capabilities
- Describe features of computer vision workloads on Azure
- Identify common types of computer vision solution
- Identify Azure tools and services for computer vision tasks
- Describe features of Natural Language Processing (NLP) workloads on Azure
- Identify features of common NLP Workload Scenarios
- Identify Azure tools and services for NLP workloads
- Describe features of generative AI workloads on Azure
- Identify features of generative AI solutions
- Identify capabilities of Azure OpenAI Service
