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DP-100 Exam Cram - Designing and Implementing a Data Science Solution on Azure | Practice Test with Actual Questions

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

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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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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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