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.

  
  

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.

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.

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.

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).

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).