AI-900 Azure AI Fundamentals Practice Exam Questions 2025
Course Description
Prepare for the AI-900 or AI 900 exam with confidence! This set includes 324 unique free 2026 digital sat prep practice questions math and english course 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 risks in cybersecurity 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).
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