AI 900 Azure AI Fundamentals Exam Preparation Course, AI-900 Azure AI Fundamentals with 324 Practice Exam Questions
Prepare for the AI-900 or AI 900 exam with confidence! This set includes 324 unique practice questions created from scratch and fully compliant with the official 2025 exam syllabus.
What you’ll learn
- From Video Quiz, Students will Gain Confidence Face Real Exam Question.
- Attend Original Exam like Question.
- Practice with more than 300 Questions.
- Learn from the explanation provided in each solution.
Course Content
- AI-900 Azure AI Fundamentals – Quiz Set 1 Video Quiz –> 7 lectures • 49min.
- AI-900 Azure AI Fundamentals – Quiz Set 2 –> 0 lectures • 0min.
- AI-900 Azure AI Fundamentals – Quiz Set 3 –> 0 lectures • 0min.
- AI-900 Azure AI Fundamentals – Quiz Set 4 –> 0 lectures • 0min.
- AI-900 Azure AI Fundamentals – Quiz Set 5 –> 0 lectures • 0min.
- AI-900 Azure AI Fundamentals – Quiz Set 6 –> 0 lectures • 0min.
- Practice Set 1 – AI-900 Azure AI Fundamentals –> 0 lectures • 0min.
- Practice Set 2 – AI-900 Azure AI Fundamentals –> 0 lectures • 0min.
Requirements
Prepare for the AI-900 or AI 900 exam with confidence! This set includes 324 unique practice questions created from scratch and fully compliant with the official 2025 exam syllabus.
The AI-900 exam syllabus is structured around five main domains, covering core AI/ML concepts and how they are implemented using Microsoft Azure AI services.
Domain Approximate Weighting
1. Describe Artificial Intelligence workloads and considerations 15-20%
2. Describe fundamental principles of machine learning on Azure 15-20%
3. Describe features of computer vision workloads on Azure 15-20%
4. Describe features of Natural Language Processing (NLP) workloads on Azure 15-20%
5. Describe features of generative AI workloads on Azure 20-25%
1. Describe Artificial Intelligence workloads and considerations (15-20%)
- Identify features of common AI workloads: computer vision, NLP, document processing, generative AI.
- Identify guiding principles for responsible AI: fairness, reliability & safety, privacy & security, inclusiveness, transparency, accountability.
2. Describe fundamental principles of machine learning on Azure (15-20%)
- Identify common machine learning techniques: regression, classification, clustering, deep learning, Transformer architecture.
- Describe core machine learning concepts: features and labels, training vs validation datasets.
- Describe Azure Machine Learning capabilities: automated ML, data & compute services, model management & deployment.
3. Describe features of computer vision workloads on Azure (15-20%)
- Identify types of computer vision solutions: image classification, object detection, OCR, facial detection/analysis.
- Identify Azure tools & services: e.g., Azure AI Vision, Azure AI Face detection service.
4. Describe features of Natural Language Processing (NLP) workloads on Azure (15-20%)
- Identify features & uses of NLP scenarios: key phrase extraction, entity recognition, sentiment analysis, language modelling, speech recognition & synthesis, translation.
- Identify Azure tools & services for NLP workloads: e.g., Azure AI Language, Azure AI Speech.
5. Describe features of generative AI workloads on Azure (20-25%)
- Identify features of generative AI models and common use-cases.
- Identify generative AI services/capabilities in Azure: e.g., Azure OpenAI Service, Azure AI Foundry (model catalog).