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Microsoft DP-100 : Designing and Implementing a Data Science Solution on Azure Exam

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Test Number : DP-100
Test Name : Designing and Implementing a Data Science Solution on Azure
Vendor Name : Microsoft
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DP-100 test Format | DP-100 Course Contents | DP-100 Course Outline | DP-100 test Syllabus | DP-100 test Objectives


Set up an Azure Machine Learning workspace (30-35%)
Create an Azure Machine Learning workspace
• create an Azure Machine Learning workspace
• configure workspace settings
• manage a workspace by using Azure Machine Learning Studio
Manage data objects in an Azure Machine Learning workspace
• register and maintain data stores
• create and manage datasets
Manage experiment compute contexts
• create a compute instance
• determine appropriate compute specifications for a training workload
• create compute targets for experiments and training

Run experiments and train models (25-30%)
Create models by using Azure Machine Learning Designer
• create a training pipeline by using Designer
• ingest data in a Designer pipeline
• use Designer modules to define a pipeline data flow
• use custom code modules in Designer
Run training scripts in an Azure Machine Learning workspace
• create and run an experiment by using the Azure Machine Learning SDK
• consume data from a data store in an experiment by using the Azure Machine Learning
SDK
• consume data from a dataset in an experiment by using the Azure Machine Learning
SDK
• choose an estimator
Generate metrics from an experiment run
• log metrics from an experiment run
• retrieve and view experiment outputs
• use logs to troubleshoot experiment run errors
Automate the model training process
• create a pipeline by using the SDK
• pass data between steps in a pipeline
• run a pipeline
• monitor pipeline runs

Optimize and manage models (20-25%)
Use Automated ML to create optimal models
• use the Automated ML interface in Studio
• use Automated ML from the Azure ML SDK
• select scaling functions and pre-processing options
• determine algorithms to be searched
• define a primary metric
• get data for an Automated ML run
• retrieve the best model
Use Hyperdrive to rune hyperparameters
• select a sampling method
• define the search space
• define the primary metric
• define early termination options
• find the model that has optimal hyperparameter values
Use model explainers to interpret models
• select a model interpreter
• generate feature importance data
Manage models
• register a trained model
• monitor model history
• monitor data drift

Deploy and consume models (20-25%)
Create production compute targets
• consider security for deployed services
• evaluate compute options for deployment
Deploy a model as a service
• configure deployment settings
• consume a deployed service
• troubleshoot deployment container issues
Create a pipeline for batch inferencing
• publish a batch inferencing pipeline
• run a batch inferencing pipeline and obtain outputs
Publish a Designer pipeline as a web service
• create a target compute resource
• configure an Inference pipeline
• consume a deployed endpoint

Set up an Azure Machine Learning workspace (30-35%)
Create an Azure Machine Learning workspace
• create an Azure Machine Learning workspace
• configure workspace settings
• manage a workspace by using Azure Machine Learning sStudio
Manage data objects in an Azure Machine Learning workspace
• register and maintain data stores
• create and manage datasets
Manage experiment compute contexts
• create a compute instance
• determine appropriate compute specifications for a training workload
• create compute targets for experiments and training

Run experiments and train models (25-30%)
Create models by using Azure Machine Learning Designer
• create a training pipeline by using Azure Machine Learning Ddesigner
• ingest data in a Designer designer pipeline
• use Designer designer modules to define a pipeline data flow
• use custom code modules in Designer designer
Run training scripts in an Azure Machine Learning workspace
• create and run an experiment by using the Azure Machine Learning SDK
• consume data from a data store in an experiment by using the Azure Machine Learning
SDK
• consume data from a dataset in an experiment by using the Azure Machine Learning
SDK
• choose an estimator for a training experiment
Generate metrics from an experiment run
• log metrics from an experiment run
• retrieve and view experiment outputs
• use logs to troubleshoot experiment run errors
Automate the model training process
• create a pipeline by using the SDK
• pass data between steps in a pipeline
• run a pipeline
• monitor pipeline runs

Optimize and manage models (20-25%)
Use Automated ML to create optimal models
• use the Automated ML interface in Azure Machine Learning Studiostudio
• use Automated ML from the Azure Machine Learning SDK
• select scaling functions and pre-processing options
• determine algorithms to be searched
• define a primary metric
• get data for an Automated ML run
• retrieve the best model
Use Hyperdrive to rune tune hyperparameters
• select a sampling method
• define the search space
• define the primary metric
• define early termination options
• find the model that has optimal hyperparameter values
Use model explainers to interpret models
• select a model interpreter
• generate feature importance data
Manage models
• register a trained model
• monitor model history
• monitor data drift

Deploy and consume models (20-25%)
Create production compute targets
• consider security for deployed services
• evaluate compute options for deployment
Deploy a model as a service
• configure deployment settings
• consume a deployed service
• troubleshoot deployment container issues
Create a pipeline for batch inferencing
• publish a batch inferencing pipeline
• run a batch inferencing pipeline and obtain outputs
Publish a Designer designer pipeline as a web service
• create a target compute resource
• configure an Inference pipeline
• consume a deployed endpoint



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Microsoft Implementing test

massive tech accused of heading off $2.8bn in tax to poorest countries | DP-100 test dumps and Test Prep

massive US expertise organizations are exploiting loopholes in global tax suggestions to evade paying as an awful lot as $2.8bn (Β£2.1bn) tax a year in developing countries, in keeping with research by using the anti-poverty charity ActionAid foreign.

facebook, Google and Microsoft have been accused of failing to pay a fair quantity of taxes in terrible nations the place governments are struggling to deliver even primary healthcare or training to their residents.

ActionAid overseas spoke of its analysis showed the salary misplaced from the β€œtax gap” might have been used to pay for more than seven hundred,000 new lecturers or 850,000 simple college lecturers.

There isn't any recommendation that the tech establishments are breaking the rules or actively evading tax. ActionAid noted the expertise taxes are being misplaced due to world leaders’ failure to enforce international requisites on tax that would force multinational organizations to pay more tax within the countries where they generate their revenue.

β€œLittle is commonly used about how plenty tax these businesses are presently paying in developing international locations, as they are nonetheless now not required to publicly divulge this guidance,” ActionAid said. β€œThis analysis indicates, youngsters, that billions may well be at stake within the lengthy overdue reform of overseas corporate taxation – satisfactory to seriously change underfunded health and education techniques in one of the most world’s poorest international locations.”

David Archer, international taxation spokesman for ActionAid overseas, said: β€œladies and young individuals are paying the price for an out of date device that has allowed big tech agencies, including giants like facebook, [Google parent] Alphabet and Microsoft, to rack up huge profits throughout the pandemic, whereas contributing little or nothing against public functions in countries within the world south.

β€œThe $2.8bn tax hole is only the tip of the iceberg – this analysis covers handiest three tech giants. but by myself, the funds that fb, Alphabet and Microsoft would be paying beneath fairer tax rules could transform public services for thousands and thousands of individuals.”

register to the every day enterprise nowadays e mail or follow Guardian company on Twitter at @BusinessDesk

India, Indonesia, Brazil, Nigeria and Bangladesh are the international locations with the highest tax gaps involving these three corporations. the world fitness corporation estimates that the 20 countries studied by way of ActionAid need to employ as a minimum 1,790,000 extra nurses by 2030 to achieve their benchmark of forty nurses per 10,000 americans. These shortages may well be wholly covered in just three years if international tax guidelines allowed reasonable taxing of those three massive tech organizations and the income become allocated for this goal.

The expertise businesses haven't replied to requests for remark.


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