[100% Off] Ai-901 Azure Ai Practice Exams 2026 (Ai-900 Updated)
AI 900 Azure AI Fundamentals Exam Preparation Course, AI-900 Azure AI Fundamentals with 324 Practice Exam Questions
Requirements
- Familiarity with cloud computing, AI and Azure services will significantly aid your preparation.
Description
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 2026 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).
Author(s): Codaming • 300k+ Learners Worldwide, Vedika Singh, Dinesh Kumar
