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DP-100 exam Format | DP-100 Course Contents | DP-100 Course Outline | DP-100 exam Syllabus | DP-100 exam 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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