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所在平台: Udemy |
课程主页: https://www.udemy.com/course/dp-100-microsoft-azure-data-scientist-practice-exams/
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**DP-100: Microsoft Azure Data Scientist Associate 备考练习题** **课程概述:** 本课程旨在帮助您准备 Microsoft Azure Data Scientist Associate (DP-100) 认证考试。此认证面向希望展示其使用 Azure 技术构建和部署机器学习模型专业知识的个人。通过本课程，您将学习如何在 Azure 环境中设计、实现和部署机器学习解决方案，涵盖数据探索、数据准备、模型训练、模型部署与再训练等关键领域。 **目标受众:** * 希望获得 Azure Data Scientist Associate 认证的初级或资深数据科学家。 * 需要了解如何在 Azure 中利用大数据、实现数据科学解决方案以及创建智能应用程序的专业人士。 **考试信息:** * **考试名称：** Microsoft Certified: Azure Data Scientist Associate * **考试代码：** DP-100 * **考试语言：** 英语、日语、韩语、简体中文 * **考试形式：** 选择题、多项选择题 * **题目数量：** 40-60 题（估算） * **考试时长：** 120 分钟 * **合格分数：** 700-1000 **主要学习内容（考试大纲）：** 1. **设计和准备机器学习解决方案 (20-25%)** * 确定训练工作负载的适当计算规格。 * 描述模型部署需求。 * 选择用于构建或训练模型的开发方法。 * 管理 Azure 机器学习工作区（创建、配置 Git 集成、管理注册表、管理 Datastores 和 Data Assets）。 * 管理实验的计算资源（创建 Compute Targets，配置 Spark Pools，选择环境）。 2. **探索数据和训练模型 (35-40%)** * 使用 Data Assets 和 Datastores 探索数据。 * 进行数据访问和整理（包括使用 Apache Spark）。 * 使用 Azure 机器学习 Designer 创建模型（管道、自定义代码组件、评估模型）。 * 使用 AutoML 探索最佳模型（表格数据、计算机视觉、自然语言处理）。 * 使用 Notebook 进行自定义模型训练（使用 Compute Instance、MLflow 跟踪、Python SDK v2）。 * 使用 Azure 机器学习进行超参数调优（采样方法、搜索空间、主度量、提前终止）。 3. **准备模型进行部署 (20-25%)** * 运行模型训练脚本（配置作业运行设置、计算、数据消耗）。 * 使用 MLflow 记录指标和记录。 * 配置作业环境。 * 实现训练管道（创建管道、传递数据、监视运行）。 * 创建自定义组件和组件型管道。 * 管理 Azure 机器学习中的模型（描述 MLflow 模型输出、选择打包框架、评估模型）。 4. **部署和再训练模型 (10-15%)** * 部署模型（在线终结点、批量终结点）。 * 配置部署设置和计算。 * 测试已部署的模型。 * 应用 MLOps 实践（触发 Azure 机器学习作业，自动化模型再训练）。 **练习题价值:** DP-100 备考练习题提供了一个全面、真实的考试模拟环境，帮助您： * 评估自身知识水平，识别学习薄弱环节。 * 熟悉考试形式和题目难度。 * 通过详细的题目解析，理解正确答案的推理过程。 * 制定有效的备考策略，提高时间管理和题目选择能力。 * 增强信心，提高通过 Microsoft Azure Data Scientist Associate 认证考试的几率。 本练习题套件包含多种练习模式，如计时模式和复习模式，以满足不同学习者的需求。
DP-100: Microsoft Azure Data Scientist Associate certification is designed for individuals who want to demonstrate their expertise in using Azure technologies to build and deploy machine learning models. This certification validates the skills required to work with big data, implement data science solutions, and utilize Azure services to create intelligent applications. With this certification, data scientists can showcase their ability to leverage Azure's powerful tools and services to solve complex business problems.DP-100 certification covers a wide range of topics that are essential for data scientists working with Azure. Candidates will learn how to design and implement machine learning models, perform data exploration and visualization, and deploy models into production environments. They will also gain knowledge in using Azure Machine Learning service, Azure Databricks, and other Azure services to build end-to-end data science solutions.DP-100: Microsoft Azure Data Scientist Associate Practice Exam is a comprehensive and reliable resource designed to help aspiring data scientists prepare for the Microsoft Azure Data Scientist Associate certification exam. This practice exam is specifically tailored to cover all the essential topics and skills required to excel in the real exam. It provides a realistic simulation of the actual exam environment, allowing candidates to familiarize themselves with the format and difficulty level of the questions they will encounter.This practice exam consists of a wide range of questions that assess the candidate's knowledge and understanding of various concepts related to data science in the Azure environment. It covers key areas such as data exploration and visualization, data preparation, modeling, and machine learning implementation. Each question is carefully crafted to test the candidate's ability to apply their knowledge to real-world scenarios and solve complex problems using Azure tools and services.DP-100: Microsoft Azure Data Scientist Associate Practice Exam, candidates can assess their readiness for the certification exam and identify areas where they need to focus their study efforts. The practice exam provides detailed explanations for each question, helping candidates understand the reasoning behind the correct answers and learn from their mistakes. Additionally, it offers valuable insights into the exam structure and content, enabling candidates to develop effective strategies for time management and question prioritization. Whether you are a beginner or an experienced data scientist, this practice exam is an invaluable tool to enhance your skills and increase your chances of success in the Microsoft Azure Data Scientist Associate certification exam.DP-100: Microsoft Azure Data Scientist Associate Exam Summary:Exam Name: Microsoft Certified - Azure Data Scientist AssociateExam code: DP-100Exam voucher cost: $165 USDExam languages: English, Japanese, Korean, and Simplified ChineseExam format: Multiple-choice, multiple-answerNumber of questions: 40-60 (estimate)Length of exam: 120minutesPassing grade: Score is from 700-1000.DP-100: Microsoft Azure Data Scientist Associate Exam Syllabus Topics:Design and prepare a machine learning solution (20-25%)Explore data and train models (35-40%)Prepare a model for deployment (20-25%)Deploy and retrain a model (10-15%)Design and prepare a machine learning solution (20-25%)Design a machine learning solutionDetermine the appropriate compute specifications for a training workloadDescribe model deployment requirementsSelect which development approach to use to build or train a modelManage an Azure Machine Learning workspaceCreate an Azure Machine Learning workspaceManage a workspace by using developer tools for workspace interactionSet up Git integration for source controlCreate and manage registriesManage data in an Azure Machine Learning workspaceSelect Azure Storage resourcesRegister and maintain datastoresCreate and manage data assetsManage compute for experiments in Azure Machine LearningCreate compute targets for experiments and trainingSelect an environment for a machine learning use caseConfigure attached compute resources, including Apache Spark poolsMonitor compute utilizationExplore data and train models (35-40%)Explore data by using data assets and data storesAccess and wrangle data during interactive developmentWrangle interactive data with Apache SparkCreate models by using the Azure Machine Learning designerCreate a training pipelineConsume data assets from the designerUse custom code components in designerEvaluate the model, including responsible AI guidelinesUse automated machine learning to explore optimal modelsUse automated machine learning for tabular dataUse automated machine learning for computer visionUse automated machine learning for natural language processingSelect and understand training options, including preprocessing and algorithmsEvaluate an automated machine learning run, including responsible AI guidelinesUse notebooks for custom model trainingDevelop code by using a compute instanceTrack model training by using MLflowEvaluate a modelTrain a model by using Python SDKv2Use the terminal to configure a compute instanceTune hyperparameters with Azure Machine LearningSelect a sampling methodDefine the search spaceDefine the primary metricDefine early termination optionsPrepare a model for deployment (20-25%)Run model training scriptsConfigure job run settings for a scriptConfigure compute for a job runConsume data from a data asset in a jobRun a script as a job by using Azure Machine LearningUse MLflow to log metrics from a job runUse logs to troubleshoot job run errorsConfigure an environment for a job runDefine parameters for a jobImplement training pipelinesCreate a pipelinePass data between steps in a pipelineRun and schedule a pipelineMonitor pipeline runsCreate custom componentsUse component-based pipelinesManage models in Azure Machine LearningDescribe MLflow model outputIdentify an appropriate framework to package a modelAssess a model by using responsible AI guidelinesDeploy and retrain a model (10-15%)Deploy a modelConfigure settings for online deploymentConfigure compute for a batch deploymentDeploy a model to an online endpointDeploy a model to a batch endpointTest an online deployed serviceInvoke the batch endpoint to start a batch scoring jobApply machine learning operations (MLOps) practicesTrigger an Azure Machine Learning job, including from Azure DevOps or GitHubAutomate model retraining based on new data additions or data changesDefine event-based retraining triggersDP-100: Microsoft Azure Data Scientist Associate Practice Exam, candidates can assess their knowledge and identify areas where they need to improve. Each question is accompanied by detailed explanations and references to relevant study materials, enabling learners to understand the reasoning behind the correct answers. Additionally, the practice exam offers timed mode and review mode, allowing candidates to simulate real exam conditions or review their answers at their own pace. Overall, the DP-100: Microsoft Azure Data Scientist Associate Practice Exam is an invaluable tool for anyone aspiring to become a certified Azure Data Scientist Associate. It provides a comprehensive and realistic practice experience, helping candidates build confidence and enhance their chances of success in the official certification exam.
