课程详情

DP-100: Microsoft Azure Data Scientist Associate certification is a highly sought-after credential for professionals looking to demonstrate their expertise in data science and analytics using the Microsoft Azure platform. This certification is designed for individuals who have a strong background in data science and machine learning, and who are looking to advance their skills in working with data on the Azure cloud.One of the key features of the DP-100 certification is the comprehensive practice exam that is included as part of the preparation process. This practice exam is designed to simulate the experience of taking the actual DP-100 exam, giving candidates the opportunity to familiarize themselves with the format and structure of the test. By taking the practice exam, candidates can identify areas where they may need to focus their study efforts, and gain confidence in their ability to successfully pass the certification exam.DP-100 certification exam covers a wide range of topics related to data science and machine learning on the Azure platform. Candidates will be tested on their ability to design and implement data models, build and deploy machine learning models, and work with data in a variety of formats. The exam also covers topics such as data visualization, feature engineering, and model evaluation, ensuring that candidates have a comprehensive understanding of the key concepts and techniques used in data science.In order to prepare for the DP-100 exam, candidates are encouraged to take advantage of the resources available through Microsoft's official training program. This program includes a series of online courses and tutorials that cover the key topics and skills needed to pass the exam. Candidates can also access practice exams and other study materials to help them prepare for the certification exam.Once candidates have successfully passed the DP-100 exam, they will be awarded the Microsoft Azure Data Scientist Associate certification. This credential is recognized by employers and industry professionals as a mark of expertise in data science and analytics on the Azure platform. With the DP-100 certification, professionals can demonstrate their ability to work with data effectively, build and deploy machine learning models, and drive insights and value for their organizations.DP-100: Microsoft Azure Data Scientist Associate certification is a valuable credential for professionals looking to advance their careers in data science and analytics. With a comprehensive practice exam, a wide range of topics covered, and access to Microsoft's official training program, candidates can prepare effectively for the certification exam and demonstrate their expertise in working with data on the Azure platform. By earning the DP-100 certification, professionals can enhance their skills, advance their careers, and stand out in the competitive field of data science.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 triggersIn conclusion, the DP-100: Microsoft Azure Data Scientist Associate certification is a valuable credential for professionals looking to advance their careers in data science and analytics. With a comprehensive practice exam, a wide range of topics covered, and access to Microsoft's official training program, candidates can prepare effectively for the certification exam and demonstrate their expertise in working with data on the Azure platform. By earning the DP-100 certification, professionals can enhance their skills, advance their careers, and stand out in the competitive field of data science.

课程标签

0人关注该课程

主题相关的课程