Overview

Exam AI-900: Microsoft Azure AI Fundamentals Tests 2025 (Hotspot , Drag drop with full explanations details included )This exam is an opportunity for you to demonstrate knowledge of machine learning and AI concepts and related Microsoft Azure services. As a candidate for this exam, you should have familiarity with Exam AI-900's self-paced or instructor-led learning material.This exam is intended for you if you have both technical and non-technical backgrounds. Data science and software engineering experience are not required. However, you would benefit from having awareness of:Basic cloud conceptsClient-server applicationsYou can use Azure AI Fundamentals to prepare for other Azure role-based certifications like Azure Data Scientist Associate or Azure AI Engineer Associate, but it's not a prerequisite for any of them.Skills measuredDescribe Artificial Intelligence workloads and considerations (15-20%)Identify features of common AI workloadsIdentify computer vision workloadsIdentify natural language processing workloadsIdentify document processing workloadsIdentify features of generative AI workloadsIdentify guiding principles for responsible AIDescribe considerations for fairness in an AI solutionDescribe considerations for reliability and safety in an AI solutionDescribe considerations for privacy and security in an AI solutionDescribe considerations for inclusiveness in an AI solutionDescribe considerations for transparency in an AI solutionDescribe considerations for accountability in an AI solutionDescribe fundamental principles of machine learning on Azure (15-20%)Identify common machine learning techniquesIdentify regression machine learning scenariosIdentify classification machine learning scenariosIdentify clustering machine learning scenariosIdentify features of deep learning techniquesIdentify features of the Transformer architectureDescribe core machine learning conceptsIdentify features and labels in a dataset for machine learningDescribe how training and validation datasets are used in machine learningDescribe Azure Machine Learning capabilitiesDescribe capabilities of automated machine learningDescribe data and compute services for data science and machine learningDescribe model management and deployment capabilities in Azure Machine LearningDescribe features of computer vision workloads on Azure (15-20%)Identify common types of computer vision solutionIdentify features of image classification solutionsIdentify features of object detection solutionsIdentify features of optical character recognition solutionsIdentify features of facial detection and facial analysis solutionsIdentify Azure tools and services for computer vision tasksDescribe capabilities of the Azure AI Vision serviceDescribe capabilities of the Azure AI Face detection serviceDescribe features of Natural Language Processing (NLP) workloads on Azure (15-20%)Identify features of common NLP Workload ScenariosIdentify features and uses for key phrase extractionIdentify features and uses for entity recognitionIdentify features and uses for sentiment analysisIdentify features and uses for language modelingIdentify features and uses for speech recognition and synthesisIdentify features and uses for translationIdentify Azure tools and services for NLP workloadsDescribe capabilities of the Azure AI Language serviceDescribe capabilities of the Azure AI Speech serviceDescribe features of generative AI workloads on Azure (20-25%)Identify features of generative AI solutionsIdentify features of generative AI modelsIdentify common scenarios for generative AIIdentify responsible AI considerations for generative AIIdentify generative AI services and capabilities in Microsoft AzureDescribe features and capabilities of Azure AI FoundryDescribe features and capabilities of Azure OpenAI serviceDescribe features and capabilities of Azure AI Foundry model catalog

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