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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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Microsoft DP-100 test

 

Microsoft Will build as much as 100 New records facilities every 12 months | Killexams.com Resources

Microsoft unveiled its bullish plan this week to build 50 to 100 new data facilities throughout the globe every 12 months for the foreseeable future.

ARTICLE TITLE HERE

Microsoft plans to spend billions constructing a whopping 50 to 100 new statistics centers yearly with expectations to extend its Microsoft Azure statistics center presence into 10 new countries via the end of this 12 months.

the public cloud and application giant unveiled its aggressive records middle constructed-out plan all through the launch of Microsoft’s new immersive records center digital tour event, which provides a free online tour of a simulated Microsoft statistics middle that details the business’s investments in security, sustainability, reliability and innovation akin to its a success undersea records center scan.

besides the fact that children the Redmond, Wash.-based company didn’t unveil the locations of all its new deliberate information facilities, Microsoft lately introduced plans to spend $1 billion over the next 5 years in Malaysia to construct several information middle areas. Microsoft is also building new facts facilities far and wide from Fulton County, Georgia, and San Antonio, Texas, to Israel as it continues to extend its Microsoft Azure cloud choices across the globe.

[Related: Here Are 5 People Who Could Be VMware’s New CEO]

Microsoft, named in CRN’s 10 Hyperscale facts center corporations to monitor in 2021, operates a complete of more than 200 information centers across 34 counties.

Microsoft is one the biggest spenders in the records core market nowadays alongside the likes of Amazon net capabilities (AWS) and Google. The global COVID-19 pandemic spurred a checklist-breaking third quarter 2020 as statistics middle spending reached $37 billion. AWS, Microsoft and Google together now account for greater than 50 percent of the realm’s largest statistics centers throughout the globe, in line with data from market analysis company Synergy analysis neighborhood.

global spending on public cloud infrastructure is also hitting new heights. Public cloud infrastructure spending reached $13.3 billion within the third quarter of 2020, representing a rise of 13 p.c yr-over-yr, in accordance with IT research enterprise IDC.

Microsoft corporate vice president, Noelle Walsh, who leads the team that builds and operates the enterprise’s cloud infrastructure, spoke of the enterprise is on pace to build between 50 and one hundred new records facilities every year for the foreseeable future. Walsh did not specify if the brand new statistics core aim covered consolidations, renovations to older facilities or partnerships with other organizations.

Microsoft led the nation in 2020 in leasing extra records middle capability than another enterprise in the U.S. The tech massive leased 178 megawatts of capability in Virginia by myself in quite a few records facilities, in addition to leasing in other massive U.S. markets including the Bay area and Phoenix.

Microsoft is at present making ready to start the massive $10 billion department of defense’s Joint enterprise defense Infrastructure (JEDI) cloud contract it has been awarded on two occasions, while it additionally was currently awarded a $21.9 billion contract from the U.S. army to provide headsets according to its HoloLens 2 blended fact device.


 



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