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所在平台: Udemy |
课程主页: https://www.udemy.com/course/ai-900-microsoft-azure-ai-fundamentals-exam-test/
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课程名称：AI-900 Microsoft Azure AI基础知识考试测试2025 课程概述：本课程专注于AI-900考试，即微软Azure AI基础知识考试（2025年版）。该考试为考生提供了展示机器学习和人工智能（AI）概念及相关微软Azure服务知识的机会。无论您是技术背景还是非技术背景的候选人，只要对考试内容有一定的了解，您都可以参加该考试。虽然不要求有数据科学或软件工程经验，但对基础云概念和客户端-服务器应用有一定的认知将会带来好处。 该考试是准备其他基于Azure的角色认证（如Azure数据科学家助理或Azure AI工程师助理）的良好起点，但并不是这些认证的前提条件。 技能测评内容： 1. 描述人工智能工作负载及其注意事项（15-20%） - 确定常见AI工作负载的特征 - 识别计算机视觉、自然语言处理、文档处理和生成AI工作负载 - 识别负责AI的指导原则及在AI解决方案中对公平性、可靠性、安全性、包容性、透明度和问责性等方面的考虑 2. 描述Azure上的机器学习基本原则（15-20%） - 识别常见机器学习技术及其应用场景 - 描述Azure机器学习的能力和自动化机器学习特点 - 处理机器学习中的数据集以及模型管理与部署能力 3. 描述Azure上的计算机视觉工作负载特点（15-20%） - 确定常见计算机视觉解决方案及其功能 - 识别Azure中用于计算机视觉任务的工具和服务 4. 描述Azure上的自然语言处理（NLP）工作负载特点（15-20%） - 识别NLP常见场景中的功能及它们的应用 - 描述Azure AI语言服务和语音服务的能力 5. 描述Azure上的生成AI工作负载特点（20-25%） - 识别生成AI解决方案及模型的特征 - 确定生成AI的常见场景及负责AI的考虑 - 描述Azure AI Foundry和Azure OpenAI服务的功能及能力 此课程旨在帮助学员全面理解与AI相关的基本概念、技术和工具，为考试和进一步的职业发展奠定坚实的基础。
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
