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

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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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At Killexams.com, they deliver the latest, valid, and up-to-date Microsoft Designing and Implementing a Data Science Solution on Azure Practice Tests, essential for passing the DP-100 exam and advancing your career as a certified professional within your organization. Their mission is to empower candidates to succeed in the DP-100 exam on their first attempt. The excellence of their DP-100 exam questions consistently ranks at the forefront. They deeply appreciate their customers who rely on their boot camp and VCE for their actual DP-100 exam. Killexams.com specializes in providing authentic DP-100 exam questions, ensuring their DP-100 study materials remain current and reliable. These Designing and Implementing a Data Science Solution on Azure Practice Tests are guaranteed to help you excel in the exam with top scores, supported by their premium certification preparation resources, including TestPrep Practice Tests, online test engine, and desktop test engine.







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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DP-100 Exam

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http://killexamsbraindumps.isblog.net/dp-100-designing-and-implementing-a-data-science-solution-on-azure-practice-test-by-killexams-com-14582317
https://www.coursehero.com/file/79066259/Designing-and-Implementing-a-Data-Science-Solution-on-Azure-DP-100pdf/
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