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所在平台: Udemy |
课程主页: https://www.udemy.com/course/ai-900-microsoft-azure-ai-fundamentals-95-practice-test/
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课程名称：更新版 AI-900：Microsoft Azure AI 基础实践测试 课程概述：为了增强您在AI-900考试中的信心并在实际考试中表现出色，您需要练习这些测试。这组实践测试将会不断更新，以提供更多的学习问题，基于新的题型。[已吸引3800多名学生注册此课程。] Microsoft认证：Azure AI基础 AI-900。该考试为您提供了展示对常见机器学习（ML）和人工智能（AI）工作负载及在Azure上实施它们的知识的机会。此考试适合技术和非技术背景的候选人。虽然不要求有数据科学和软件工程经验，但具备一定的编程知识或经验将会有帮助。Azure AI基础课程可以为其他Azure角色认证（如Azure数据科学家助理或Azure AI工程师助理）做准备，但并非其先决条件。 测量技能： - 描述AI工作负载和注意事项 - 描述Azure上机器学习的基本原理 - 描述Azure上计算机视觉工作负载的特性 - 描述Azure上自然语言处理（NLP）工作负载的特性 - 描述Azure上会话AI工作负载的特性 AI概述：人工智能是创建模拟人类行为和能力的软件。关键要素包括： - 机器学习：通常是AI系统的基础，是我们“教导”计算机模型从数据中进行预测和得出结论的方式。 - 异常检测：自动检测系统中错误或异常活动的能力。 - 计算机视觉：软件通过摄像机、视频和图像解读世界的能力。 - 自然语言处理：计算机解读书面或口语语言并做出相应反应的能力。 - 会话AI：软件“代理”参与对话的能力。 Azure机器学习：机器学习是大多数AI解决方案的基础。Microsoft Azure提供以下服务： - Azure机器学习服务：一个云基础平台，允许您创建、管理和发布机器学习模型。 - 自动化机器学习：这一功能使非专业人员能够快速从数据中创建机器学习模型。 - Azure机器学习设计器：提供无代码创建机器学习解决方案的界面。 - 数据和计算管理：专业数据科学家可以访问基于云的数据存储和计算资源，以大规模运行数据实验。 - 管道：软件工程师、数据科学家和IT运营专业人员能够创建管道，以管理模型的部署、训练和管理。 通过此课程，您将为获得Microsoft Azure AI基础认证打下坚实的基础。
You must practice these practice tests to get confident in AI-900 Exam and give your best in the actual exam to get certified successfully.This set of practice tests will be get updated, so you can get more questions to great learning. It's based on a new question pattern.[3800++ Students have been enrolled for this course.]Microsoft Certified: Azure AI Fundamentals AI-900This exam is an opportunity to demonstrate knowledge of common ML and AI workloads and how to implement them on Azure.This exam is intended for candidates with both technical and non-technical backgrounds. Data science and software engineering experience is not required; however, some general programming knowledge or experience would be beneficial.Azure AI Fundamentals can be used 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 measured AI 900Describe AI workloads and considerationsDescribe fundamental principles of machine learning on AzureDescribe features of computer vision workloads on AzureDescribe features of Natural Language Processing (NLP) workloads on AzureDescribe features of conversational AI workloads on Azure---------------------------------------------------------------------------------Overview of AIAI is the creation of software that imitates human behaviors and capabilities. Key elements include:Machine learning - This is often the foundation for an AI system, and is the way we "teach" a computer model to make predictions and draw conclusions from data.Anomaly detection - The capability to automatically detect errors or unusual activity in a system.Computer vision - The capability of software to interpret the world visually through cameras, video, and images.Natural language processing - The capability for a computer to interpret written or spoken language, and respond in kind.Conversational AI - The capability of a software "agent" to participate in a conversation.Azure Machine LearningMachine Learning is the basis of most AI solutions.Microsoft Azure offers the following: Azure Machine Learning Service - A cloud-based platform that allows you to create, manage and publish machine learning models. Azure Machine Learning offers the following capabilities and features:Automated machine-learning: This feature allows non-experts to create machine learning models quickly from data.Azure Machine Learning designer: An interface that allows for no-code creation of machine learning solutions.Data and compute management: Professional data scientists can access cloud-based data storage and compute resources to run code for data experiments at scale.Pipelines: Software engineers, data scientists, and IT operations professionals are able to create pipelines that can be used to manage model deployment, training, and management.
