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Exam Information
Official specifications published by Microsoft
Exam Format
45 min
700
Fundamentals
Registration
$99 USD
Pearson VUE, Certiport, or online proctoring
24 hours between attempts, maximum 5 attempts per year
Validity
Does not expire
Not required - fundamentals certification does not expire
AI-900 Exam Topics and Domains
AI-900 is organized into 5 weighted domains. Expect to work with Azure AI Language, Azure OpenAI Service, Azure AI Foundry, Azure AI Vision, and more.
1
Describe Artificial Intelligence workloads and considerations
20%
Identify features of common AI workloads
Computer vision workloadsNatural language processing workloadsDocument processing workloadsGenerative 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
FairnessReliability and SafetyPrivacy and SecurityInclusivenessTransparencyAccountability
- 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
2
Describe fundamental principles of machine learning on Azure
17.5%
Identify common machine learning techniques
RegressionClassificationClusteringDeep LearningTransformer Architecture
- 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
Features and LabelsTraining and Validation
- 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
Automated Machine LearningData and Compute ServicesModel Management and Deployment
- 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
3
Describe features of computer vision workloads on Azure
17.5%
Identify common types of computer vision solution
Image ClassificationObject DetectionOptical Character RecognitionFacial Detection and Analysis
- 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
Azure AI Vision ServiceAzure AI Face Service
- Describe capabilities of the Azure AI Vision service
- Describe capabilities of the Azure AI Face detection service
4
Describe features of Natural Language Processing (NLP) workloads on Azure
17.5%
Identify features of common NLP Workload Scenarios
Key Phrase ExtractionEntity RecognitionSentiment AnalysisLanguage ModelingSpeech Recognition and SynthesisTranslation
- 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
Azure AI Language ServiceAzure AI Speech Service
- Describe capabilities of the Azure AI Language service
- Describe capabilities of the Azure AI Speech service
5
Describe features of generative AI workloads on Azure
22.5%
Identify features of generative AI solutions
Generative AI ModelsCommon ScenariosResponsible AI for Generative AI
- 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
Azure AI FoundryAzure OpenAI ServiceAzure AI Foundry Model Catalog
- 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
How do I earn this certification?
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How to study for this exam?
The most effective way to prepare for AI-900 is by using the PlanetCert Simulator to practice questions and review detailed explanations.
What's changed on this exam?
Current Status
- ACTIVE
- Last content update: 2025-05-02
- Announcement date: 2025-05-02
Updates
- Azure OpenAI Service 2025-05-02 Major focus area - 20-25% of exam • Release date: 2025-05-02
- Azure AI Foundry GA Replacing Azure AI Studio references • Release date: 2024-11-19
- Direct Preference Optimization (DPO) Public Preview Advanced topic - minimal coverage • Release date: 2024-08-06
Who should take this exam?
This exam is typically taken by AI enthusiasts and Business stakeholders.
- Basic understanding of cloud concepts
- Familiarity with client-server applications
- General IT knowledge