ExamPrep AI-900: Microsoft AI
Online and Offline AI-900: Microsoft Azure AI Fundamentals training.
This is the entry-level certification, ideal for those starting their journey in Microsoft AI and machine learning, and for people in non-technical roles.
This course will help you to pass the AI-900: Microsoft Azure AI Fundamentals certification exam.
Manoj S. Mahajan
28+ years Experienced Trainer with 100+ certs, View full profile....
Course Description
This course is designed to help candidates to the pass the AI-900: Microsoft Azure AI Fundamentals exam in first attempt.
What you'll get in this ExamPrep training:
- Official Microsoft syllabus delivered by MCT (Microsoft Certified Trainer)
- Hands-on lab guidance to bridge theory with practical application
- Exam practice questions at the end of each module for reinforcement
- Mock exams to revise and simulate real test conditions
Audience:
- Developers, IT professionals, and data analysts new to AI/ML.
- Students and aspiring data scientists.
- Anyone interested in implementing AI solutions using the Microsoft Azure cloud.
Prerequisite:
- Basic understanding of cloud computing concepts.
- Familiarity with the Azure management console (creating VM, Storage account, Entra ID, etc.).
- Recommended: Azure Fundamentals (AZ-900) or equivalent knowledge.
AI-900 exam details:
- Certification Name: Microsoft Certified: Azure AI Fundamentals
- Exam Code: AI-900
- Level: Beginner / Entry-level
- Exam Duration: 60 minutes
- Number of Questions: 65. These are generally multiple-choice, but may include other formats like drag-and-drop.
- Passing Score: The passing score is 700 out of 1000.
- Exam Fee: The cost is generally $99 USD, though it can vary by country or region. For example, in India, the price is approximately ₹3,691 INR.
- Validity: This certification is life time valid and you don't need to appear the renewal exams.
- Scheduling: The exam can be scheduled and taken at a Pearson VUE test center or through online proctoring.
Syllabus
Please check the syllabus tab above. ☝AI-900 Skills at a glance:
Describe Artificial Intelligence workloads and considerations (15–20%)
Describe fundamental principles of machine learning on Azure (15–20%)
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 (20–25%)
What You'll Learn
Describe Artificial Intelligence workloads and considerations (15–20%)
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
Describe fundamental principles of machine learning on Azure (15-20%)
Identify common machine learning techniques
Identify regression machine learning scenarios
Identify classification machine learning scenarios
Identify clustering machine learning scenarios
Identify features of deep learning techniques
Identify features of the Transformer architecture
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 Azure Machine Learning capabilities
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
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
Identify Azure tools and services for computer vision tasks
Describe capabilities of the Azure AI Vision service
Describe capabilities of the Azure AI Face detection service
Describe features of Natural Language Processing (NLP) workloads on Azure (15–20%)
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
Describe capabilities of the Azure AI Language service
Describe capabilities of the Azure AI Speech service
Describe features of Natural Language Processing (NLP) workloads on Azure (15–20%)
Identify features of generative AI solutions
Identify features of generative AI models
Identify common scenarios for generative AI
Identify responsible AI considerations for generative AI
Identify generative AI services and capabilities in Microsoft Azure
Describe features and capabilities of Azure AI Foundry
Describe features and capabilities of Azure OpenAI service
Describe features and capabilities of Azure AI Foundry model catalog
Poonam
Overall a very informative training session. Course content got well covered and also demonstrated the concept very well. Read more....
Ganesh
It was good experience with trainer and teaching skill good and interact and answering every questions asked by student. Good knowledge and updated with his reliable courses. Read more....
Diptikesh
It was an excellent experience to have a training from Certification Guru. Thank you very much. Read more....
