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所在平台: Udemy |
课程主页: https://www.udemy.com/course/ai-900-microsoft-azure-ai-fundamentals-practice-exams-x/
课程评论：没有评论
AI-900：Azure AI基础知识实践测试 - 简单/中等难度 课程概述 AI-900课程是由微软提供的一个前沿认证课程，旨在帮助个人掌握人工智能（AI）概念的基础知识和技能，了解如何在Microsoft Azure中实施这些概念。该课程适合希望在AI领域起步的新人或希望提升现有技能的专业人士。 课程内容涵盖多个主题，包括AI原理、机器学习、计算机视觉、自然语言处理和对话式AI。学员将学习如何使用Azure Machine Learning构建、训练和部署机器学习模型，以及如何使用Azure Cognitive Services为应用程序增加AI能力。 AI-900课程的一个重要特点是提供全面的实践考试，学员可以在参加官方认证考试前测试自己的知识和技能。该实践考试模拟真正考试的格式和难度，让学员能够识别需要重点复习的领域。 此外，AI-900课程还包含动手实验和练习，让学员在实践中应用所学知识。这些实验涵盖了构建机器学习模型、创建计算机视觉应用程序和使用Azure Cognitive Services实现聊天机器人的多个主题。 本课程由在AI技术和Microsoft Azure领域拥有丰富经验的行业专家授课，讲师分享宝贵的见解和实际案例，帮助学员更好地理解AI在不同产业和应用中的使用方式。 顺利完成AI-900课程后，学员将为参加官方的Microsoft Azure AI基础知识认证考试做好准备。该考试将测试学员对AI概念的理解以及他们使用Microsoft Azure实施AI解决方案的能力。通过获得此认证，学员可展示其在AI技术方面的熟练程度，从而提高在快速发展的AI领域的就业机会。 AI-900考试的相关信息： - 考试名称：Microsoft Certified - Azure AI Fundamentals - 考试代码：AI-900 - 考试费用：$99（美元） - 问题数量：最多40-60道题 - 题型：单选、多选、拖放以及基于性能的题目 - 考试时长：60分钟 - 语言：英语和日语 考试通过分数：700 / 1000 AI-900课程是一个全面而实用的AI概念和技术入门课程，专注于动手学习和实际应用，非常适合希望在这一令人兴奋和迅速发展的领域打下坚实基础并提升职业发展的个人。
AI-900: Microsoft Azure AI Fundamentals is a cutting-edge certification course designed to equip individuals with the foundational knowledge and skills needed to understand artificial intelligence (AI) concepts and how they are implemented in Microsoft Azure. This course is ideal for individuals who are looking to kickstart their career in AI or for professionals who want to enhance their existing skill set.AI-900 course covers a wide range of topics, including AI principles, machine learning, computer vision, natural language processing, and conversational AI. Students will learn how to build, train, and deploy machine learning models using Azure Machine Learning, as well as how to use Azure Cognitive Services to add AI capabilities to their applications.One of the key features of the AI-900 course is the comprehensive practice exam that allows students to test their knowledge and skills before taking the official certification exam. This practice exam is designed to simulate the format and difficulty level of the real exam, giving students the opportunity to identify areas where they may need to focus their study efforts.In addition to this practice exam, the AI-900 course also includes hands-on labs and exercises that allow students to apply their knowledge in a practical setting. These labs cover a variety of topics, such as building a machine learning model, creating a computer vision application, and implementing a chatbot using Azure Cognitive Services.AI-900 course is taught by industry experts who have extensive experience working with AI technologies and Microsoft Azure. These instructors provide valuable insights and real-world examples that help students better understand how AI is used in different industries and applications.Upon successful completion of the AI-900 course, students will be prepared to take the official Microsoft Azure AI Fundamentals certification exam. This exam tests students on their knowledge of AI concepts and their ability to implement AI solutions using Microsoft Azure. By earning this certification, students can demonstrate their proficiency in AI technologies and increase their employment opportunities in the rapidly growing field of AI.AI-900: Microsoft Azure AI Fundamentals Exam details:Exam Name: Microsoft Certified - Azure AI FundamentalsExam Code: AI-900Exam Price: $99 (USD)Number of Questions: Maximum of 40-60 questions,Type of Questions: Multiple Choice Questions (single and multiple response), drag and drops and performance-based,Length of Test: 60 Minutes. The exam is available in English and Japanese languages.Passing Score: 700 / 1000Languages: English, Japanese, Korean, and Simplified ChineseSchedule Exam: Pearson VUEAI-900: Microsoft Azure AI Fundamentals Certification Exams skill questions:Skill Measurement Exam Topics:-Describe Artificial Intelligence workloads and considerations (15-20%)Describe fundamental principles of machine learning on Azure (20-25%)Describe features of computer vision workloads on Azure (15-20%)Describe features of Natural Language Processing (NLP) workloads on Azure (15-20%)Describe features of generative AI workloads on Azure (15-20%)Describe Artificial Intelligence workloads and considerations (15-20%)Identify features of common AI workloadsIdentify features of content moderation and personalization workloadsIdentify computer vision workloadsIdentify natural language processing workloadsIdentify knowledge mining workloadsIdentify document intelligence 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 (20-25%)Identify common machine learning techniquesIdentify regression machine learning scenariosIdentify classification machine learning scenariosIdentify clustering machine learning scenariosIdentify features of deep learning techniquesDescribe 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 (15-20%)Identify features of generative AI solutionsIdentify features of generative AI modelsIdentify common scenarios for generative AIIdentify responsible AI considerations for generative AIIdentify capabilities of Azure OpenAI ServiceDescribe natural language generation capabilities of Azure OpenAI ServiceDescribe code generation capabilities of Azure OpenAI ServiceDescribe image generation capabilities of Azure OpenAI ServiceOverall, the AI-900: Microsoft Azure AI Fundamentals course is a comprehensive and practical introduction to AI concepts and technologies. With its focus on hands-on learning and real-world applications, this course is ideal for individuals who are looking to build a solid foundation in AI and advance their career in this exciting and rapidly evolving field.
