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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 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 and boot camp

Get your Ghost images into Boot Camp | DP-100 Practice Questions and Cheatsheet

feel you're in a blended-platform atmosphere, and also you've received a library of Symantec Ghost photos fortunately tweaked and configured to your windows XP deployments. wouldn't it be extremely good in case you may get these Ghost photographs right into a form where you could use them in your Boot Camp machines? sure, Ghost photographs aren't truly hardware-unbiased, but why no longer are trying?

At my workplace, this turned into a a lot-desired outcome; the difficulty changed into that the common DOS disks utilized in Ghost imaging might not boot EFI-based mostly Intel Macs, and even bootable CDs made with Ghost commonly did not see their multicast graphic server, in the event that they came up at all.

We eventually came up with a methodology -- a little just like the Wesleyan college manner specified here, which I desire i might considered prior -- for migrating a Ghost photo over to a Mac, and thence, by the use of NetRestore or WinClone, into a layout comfortably cloned to different Macs. the important thing to this Rube Goldberg-like system, first recommended via my colleague Steven, is a free home windows bootable CD maker referred to as BartPE. examine on for more of the conversion system, and observe that Your Mileage WILL fluctuate.The BartPE "PEbuilder" device creates a standalone version of home windows on a bootable CD, designed for system administration and utility initiatives. you're going to want a duplicate of your windows XP SP2 installer disc to deliver supply information for the build. while BartPE's performance is akin to Microsoft's home windows Preinstallation environment (WinPE) tool, the BartPE setup is freely distributed; up unless these days, access to WinPE required an commercial enterprise help agreement with MS or other high priced contracts. The latest version of WinPE is built on Vista's core and theoretically bundled with company licenses for Vista, so I could try booting one of my Macs from it to peer if or not it's usable for future rounds of imaging.

To steer clear of troubles with Microsoft's licensing agreements, the BartPE construct is preset to reboot instantly after 24 hours, making it unattractive for computer use. What it lacks in staying vigor, however, it makes up for in flexibility; the disk building tool comes with templates for a lot of windows utils and contours you might want. during this case, they constructed a BartPE boot disk and adopted the instructions for adding Ghost guide via the Ghost32.exe home windows edition of the restoration utility, plus assist for external USB drives. as soon as they had the disc in hand, they accrued their Ghost images on an external (FAT32 format) USB pressure. The relaxation of the technique goes something like this:

1. Boot your sacrificial target Mac (the complete tough force gets wiped by way of the imaging technique) from an exterior FireWire disk. Reformat the inner disk as MS-DOS (FAT32) in Disk Utility.2. Reboot out of your BartPE CD. make sure the USB drive with Ghost photographs is connected at boot time.three. Use Ghost32 to repair chosen photograph to Mac's internal pressure.4. Reboot into windows from newly imaged force. do not set up Boot Camp drivers yet.5. Defragment disk the usage of home windows accent. Convert your drive to NTFS with convert.exe in case your image is fat (ours have been).6. Optionally, run Sysprep to clear driver DB and SID. in case you have not heard of Sysprep, examine this first.7. Reboot from your Mac OS X external disk. if you plan to capture your photo with NetRestore Helper, deploy the ntfsprogs package. if you're the use of Winclone, the NTFS equipment are developed-in.8. Use NetRestore Helper or Winclone to capture the home windows boot power from supply desktop.

The effect may still be a .ntfs file representing your windows picture; which you can then flip round and deploy that to other Boot Camp-partitioned machines from Mac OS X. You might also observe that nowhere during this technique did they deploy the Boot Camp drivers; in their case, that was intentional, as they wanted to take a base photograph and then set up it to distinct hardware pursuits (iMacs and MacBook execs), installing the drivers after the reality. if you consider adventurous, there may be a walkthrough here on constructing an automatic driver deployment folder... however they figured they might stop whereas they have been forward.

Our consequences had been good to date: over 20 Boot Camp deployments from a Ghost source, together with many machines used essentially in windows mode. in case you've acquired Ghost <-> Boot Camp experiences, please chime in with what labored and what failed to on your ambiance.


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