Academy | Syllabus
Microsoft Certified: Azure AI Fundamentals
The Microsoft Azure AI Fundamentals (AI-900) exam validates foundational knowledge of AI concepts and Azure services, focusing on artificial intelligence workloads, machine learning principles, computer vision, natural language processing (NLP), and generative AI. It is designed for beginners wanting to demonstrate AI literacy.
Trusted by OrganizationsProven by Professionals
+21
Years of Experiences
+6.000
Delivered Training Programs
+50.000
Professionals
90%
Exam Pass Rate
Program Details
Durations
The program is a 5-day intensive training class.
Method of Delivery
The program provided by Multimatics will be delivered through interactive presentation by professional instructor(s), group debriefs, individual and team exercises, behavior modelling and roleplays, one-to-one and group discussion, case studies, and projects.
Who Should Attend?
Beginners, technical professionals, students, and business leaders.
Program Objectives
By the end of the program, participants will be able to:
- Identify key AI principles (fairness, reliability, safety, privacy, security, inclusiveness, transparency) and common AI scenarios (e.g., knowledge mining, computer vision, natural language processing).
- Understand regression, classification, clustering, and reinforcement learning. Learn to use automated machine learning (AutoML) and Azure Machine Learning studio.
- Identify tasks like image classification, object detection, semantic segmentation, optical character recognition (OCR), and facial recognition using Azure Cognitive Services.
- Understand language modeling, sentiment analysis, key phrase extraction, text translation, and speech recognition.
- Describe large language models (LLMs), prompt engineering, and the capabilities of Azure OpenAI Service.
- Build and deploy chatbots using tools like Azure Bot Service.
Examination Details
Duration
45 Minutes
Format
- Open BookClose Book
Level
-
Passing Grade
65%
Program Modules
Describe Artificial Intelligence Workloads and Considerations
Topic Covered
- Identify features of common AI workloads
- Identify computer vision workloads
- Identify natural language processing workloads
- Identify document processing 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