Designing and Implementing a Data Science Solution on Azure

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