Description
Looking for an perfect Azure DP-100 exam preparation? Searching since hours without good results? Congratulation! You found what you need, without wasting time downloading a lot of stuff you need to sort anyway or which have wrong solutions! You get an explanation whereever needed, so you don't need to begin research on your own. The answers to the questions are validated. Microsoft Certified: Azure Data Scientist Associate - Full exam preparationReal exam details: Duration: 180 Minutes, No. Of Questions: 40-60, Passing Score: 70%. This course provides overall 340 unique questions for your Azure exam preparation! We always want to deliver highest quality to you and we made our best to do so. If you find any issue let me know and we will correct it immediatelly:)Well performing Azure practitioners have great long-term job oppertunities. Each higher level of Oracle certification brings a higher standard of benchmarking skill and ability, which leads to greater opportunities and higher pay. What people say who are certified by Microsoft Azure:23 percent received up to a 20 percent salary increase after obtaining certificationNearly 65 percent of respondents received a positive impact on their professional image or reputation after obtaining certification54 percent of those who obtained certifications experienced a career benefit within three months, and 24 percent experienced the benefit immediately. Benefeits of getting further certified: Added credibilityGood job opportunitiesAzure Experts are in DemandMore than 80 percent of Fortune 500 companies are taking advantage of Microsoft CloudData shows that around 90 percent of companies are taking some advantage of cloud technologyExam Concepts: Manage Azure resources for machine learning(25-30%)Run experiments and train models (20-25%)Deploy and operationalize machine learning solutions (35-40%)Implement responsible machine learning (5-10%)This course provide overall 540 unique questions for your exam preparation. There are no duplicated questions. All questions are multiple choice with one or several correct answer. You will get the information of how many answers are correct for each question as in the real exam. There are 5 test exams for you. Each has 60 questions as the real exam has. There sixth exam test contains 240 questions. You should do this test, after solving the first 5 tests with a very good scoreYou will learn about the following contents as they can be covered by the exam: Exam TopicsManage Azure resources for machine learning (25-30%)Create an Azure Machine Learning workspacecreate an Azure Machine Learning workspaceconfigure workspace settingsmanage a workspace by using Azure Machine Learning studioManage data in an Azure Machine Learning workspaceselect Azure storage resourcesregister and maintain datastorescreate and manage datasetsManage compute for experiments in Azure Machine Learningdetermine the appropriate compute specifications for a training workloadcreate compute targets for experiments and trainingconfigure Attached Compute resources including Azure Databricksmonitor compute utilizationImplement security and access control in Azure Machine Learningdetermine access requirements and map requirements to built-in rolescreate custom rolesmanage role membershipmanage credentials by using Azure Key VaultSet up an Azure Machine Learning development environmentcreate compute instancesshare compute instancesaccess Azure Machine Learning workspaces from other development environmentsSet up an Azure Databricks workspacecreate an Azure Databricks workspacecreate an Azure Databricks clustercreate and run notebooks in Azure Databrickslink and Azure Databricks workspace to an Azure Machine Learning workspaceRun experiments and train models (20-25%)Create models by using the Azure Machine Learning designercreate a training pipeline by using Azure Machine Learning designeringest data in a designer pipelineuse designer modules to define a pipeline data flowuse custom code modules in designerRun model training scriptscreate and run an experiment by using the Azure Machine Learning SDKconfigure run settings for a scriptconsume data from a dataset in an experiment by using the Azure Machine LearningSDKrun a training script on Azure Databricks computerun code to train a model in an Azure Databricks notebookGenerate metrics from an experiment runlog metrics from an experiment runretrieve and view experiment outputsuse logs to troubleshoot experiment run errorsuse MLflow to track experimentstrack experiments running in Azure DatabricksUse Automated Machine Learning to create optimal modelsuse the Automated ML interface in Azure Machine Learning studiouse Automated ML from the Azure Machine Learning SDKselect pre-processing optionsselect the algorithms to be searcheddefine a primary metricget data for an Automated ML runretrieve the best modelTune hyperparameters with Azure Machine Learningselect a sampling methoddefine the search spacedefine the primary metricdefine early termination optionsfind the model that has optimal hyperparameter valuesDeploy and operationalize machine learning solutions (35-40%)Select compute for model deploymentconsider security for deployed servicesevaluate compute options for deploymentDeploy a model as a serviceconfigure deployment settingsdeploy a registered modeldeploy a model trained in Azure Databricks to an Azure Machine Learning endpointconsume a deployed servicetroubleshoot deployment container issuesManage models in Azure Machine Learningregister a trained modelmonitor model usagemonitor data driftCreate an Azure Machine Learning pipeline for batch inferencingconfigure a ParallelRunStepconfigure compute for a batch inferencing pipelinepublish a batch inferencing pipelinerun a batch inferencing pipeline and obtain outputsobtain outputs from a ParallelRunStepPublish an Azure Machine Learning designer pipeline as a web servicecreate a target compute resourceconfigure an inference pipelineconsume a deployed endpointImplement pipelines by using the Azure Machine Learning SDKcreate a pipelinepass data between steps in a pipelinerun a pipelinemonitor pipeline runsApply ML Ops practicestrigger an Azure Machine Learning pipeline from Azure DevOpsautomate model retraining based on new data additions or data changesrefactor notebooks into scriptsimplement source control for scriptsImplement responsible machine learning (5-10%)Use model explainers to interpret modelsselect a model interpretergenerate feature importance dataDescribe fairness considerations for modelsevaluate model fairness based on prediction disparitymitigate model unfairnessDescribe privacy considerations for datadescribe principles of differential privacyspecify acceptable levels of noise in data and the effects on privacyIn case you have questions, do not hesitate to contact us. Please be aware that we are working on this course on an ongoing basis. We always want to deliver highest quality to you and we try our best to do so. If you find any issue let us know and we will correct it immediatelly:)
