Free Practice Questions for Microsoft Azure AI Fundamentals (AI-900) Certification
Study with 339 exam-style practice questions designed to help you prepare for the Microsoft Azure AI Fundamentals (AI-900). All questions are aligned with the latest exam guide and include detailed explanations to help you master the material.
Start Practicing
Random Questions
Practice with randomly mixed questions from all topics
Domain Mode
Practice questions from a specific topic area
Exam Information
Exam Details
Key information about Microsoft Azure AI Fundamentals (AI-900)
associate (intermediate)
English (localized versions updated approximately eight weeks after English version)
Most questions cover General Availability (GA) features; may include Preview features if commonly used.
Familiarity with Exam AI-900's self-paced or instructor-led learning material.
Candidates with technical and non-technical backgrounds; no data science or software engineering experience required. Awareness of basic cloud concepts and client-server applications is beneficial.
The bullets that follow each of the skills measured are intended to illustrate how we are assessing that skill. Related topics may be covered in the exam.
Exam Topics & Skills Assessed
Skills measured (from the official study guide)
Domain 1: Describe Artificial Intelligence workloads and considerations
Subdomain 1.1: Identify features of common AI workloads
- Identify computer vision workloads - Identify natural language processing workloads - Identify document processing workloads - Identify features of generative AI workloads
Subdomain 1.2: Identify guiding principles for responsible AI
- 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
Domain 2: Describe fundamental principles of machine learning on Azure
Subdomain 2.1: Identify common machine learning techniques
- 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
Subdomain 2.2: Describe core machine learning concepts
- Identify features and labels in a dataset for machine learning - Describe how training and validation datasets are used in machine learning
Subdomain 2.3: Describe Azure Machine Learning capabilities
- 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
Domain 3: Describe features of computer vision workloads on Azure
Subdomain 3.1: Identify common types of computer vision solution
- 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
Subdomain 3.2: Identify Azure tools and services for computer vision tasks
- Describe capabilities of the Azure AI Vision service - Describe capabilities of the Azure AI Face detection service
Domain 4: Describe features of Natural Language Processing (NLP) workloads on Azure
Subdomain 4.1: Identify features of common NLP Workload Scenarios
- 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
Subdomain 4.2: Identify Azure tools and services for NLP workloads
- Describe capabilities of the Azure AI Language service - Describe capabilities of the Azure AI Speech service
Domain 5: Describe features of generative AI workloads on Azure
Subdomain 5.1: Identify features of generative AI solutions
- Identify features of generative AI models - Identify common scenarios for generative AI - Identify responsible AI considerations for generative AI
Subdomain 5.2: Identify generative AI services and capabilities in Microsoft Azure
- 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
Techniques & products