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

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Test Number : DP-100
Test Name : Designing and Implementing a Data Science Solution on Azure
Vendor Name : Microsoft
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DP-100 test Format | DP-100 Course Contents | DP-100 Course Outline | DP-100 test Syllabus | DP-100 test 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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Microsoft Implementing questions

Microsoft updates Xbox Accessibility guidelines to help devs make games for all and sundry | DP-100 examcollection and Test Prep

Microsoft has up-to-date its Xbox Accessibility guidelines (XAGs) in keeping with remarks from its Gaming & disability group and video game developers.Β 

The guidelines have been originally launched in 2019 with the goal of featuring devs with a set of most appropriate practices to support the trade in driving accessibility forwards.Β 

They had been supposed for use via designers to assist generate ideas while also serving as a guiding guidelines for devs seeking to validate the accessibility of a undertaking.Β 

Now, Microsoft has sophisticated the guidelines in keeping with a wide-range of comments, enhancing the language used and disposing of jargonΒ to be sure the guidelines are clear and easily understood,Β enforcing enhanced scoping inquiries to assist devs establish which XAGs they should still prioritize, and offering extra background and foundational tips to make sure XAGs less demanding to comprehend.

The enterprise has additionally created implementationΒ guideline examples in the kind of photos and video clips to provide builders with real-world demonstrations of certain XAGs in action, and has added a succinct "purpose" commentary for each XAG to assist readers more simply locateΒ crucial guidelines.Β 

Microsoft has also built anΒ "trade-first, platform offered" video game accessibility checking out application that can also be used to validate video games the usage of the Xbox Accessibility guidelines.Β 

The challenge changed into spearheaded through principle software engineering lead in online game Reliability Engineering, Mike Gamble, and gives builders the alternative of sending their Xbox or notebook mission into Microsoft to be analyzed and validated against the XAGs.Β 

Any concerns that are flagged can be annotated with replica steps, screenshots, and different counsel to assist dev groups take into account what facets of their undertaking can be prove difficult to disabled players.Β 

you could find out greater about Microsoft's new-seem to be Xbox Accessibility guidelines via clicking here.


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