Simply study these DP-100 Practice Questions before test.
A standout feature of Killexams.com is their commitment to accessibility. They proudly offer 100% free DP-100 practice tests for obtain and evaluation. This allows candidates to assess their readiness without any financial commitment, giving them the confidence to proceed with their studies. Their free restheirces are designed to mimic the real test experience, providing test questions with valid anstheyrs that closely resemble those found in the real DP-100 test. This authentic practice experience is cru

Navigate the crowded landscape of online Mock Exam providers with confidence by choosing killexams.com, the premier stheirce for reliable and up-to-date DP-100 MCQs Practice Tests. Avoid wasting time and money on outdated or invalid DP-100 Actual Questions materials from less reputable stheirces. Visit killexams.com to obtain 100% free DP-100 MCQs test questions and experience their quality firsthand. Register to access the latest, authentic DP-100 Actual Questions Practice Tests, complete with real test questions and anstheyrs, and take advantage of exclusive discount coupons. Boost ytheir preparation with their DP-100 VCE test system, designed to simulate the real test environment.

Effortlessly obtain the DP-100 MCQs PDF to study on any device—iPad, iPhone, PC, smart TV, or Android—making it easy to memorize DP-100 MCQs content on the go. Maximize ytheir preparation by dedicating ample time to reviewing the DP-100 questions and anstheyrs. Repeated practice with their VCE test system ensures retention and sharpens ytheir performance for the real test. Approach the DP-100 test as if it theyre the real thing, mastering the material theyll in advance to significantly Improve ytheir score and achieve certification success.

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 bettheyen 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 theyb service

• create a target compute restheirce

• 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 bettheyen 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 theyb service

• create a target compute restheirce

• configure an Inference pipeline

• consume a deployed endpoint