| Exam Code | DP-100 |
| Questions and Answers | 354 |
| Premium Access | Yes |
| Online Test Engine | Yes |
| Comprehensive Q&A | Yes |
| Success Rate | 98% |
| Real Questions | Yes |
| Updated Regularly | Yes |
| Portable Files | ✔ |
| Unlimited Download | ✔ |
| 100% Secured | ✔ |
| Confidentiality | 100% |
| Success Guarantee | 100% |
| Any Hidden Cost | $0.00 |
| Auto Recharge | No |
| Updates Intimation | by Email |
| Technical Support | Free |
| PDF Compatibility | Windows, Android, iOS, Linux |
| Test Engine Compatibility | Mac/Windows/Android/iOS/Linux |
| Sample Questions |  |
Pass4sure Practice Tests are an effective way to prepare for the DP-100 exam. The practice tests include premium PDF and Test Engine Software. Pass4surez.in provides an extensive question bank to improve your knowledge and help you achieve high marks on the DP-100 exam.
The Designing and Implementing a Data Science Solution on Azure exam questions and answers on Pass4surez.in are regularly verified and updated to ensure they reflect the latest syllabus and topics covered in the real test. The certification exams and entry test exams from pass4surez.com make you familiar with the test environment. The goal is to enhance your knowledge of the DP-100 exam and enable you to pass it on your first attempt.
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CERTIFICATION EXAM PREMIUM PDF
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CERTIFICATION EXAM TEST ENGINE
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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
You can download a free PDF of the DP-100 practice test and study guide to try before purchasing the premium files. To ace the exam, simply download the DP-100 exam questions and answers file, memorize the content, and practice with the VCE Exam Simulator. This will ensure you are fully prepared for the real test.
The DP-100 PDF practice test and exam questions and answers can be accessed on any device, including iPhone, iPad, Android, and Windows. You can download the PDF to your computer or any other device and start studying. Additionally, you can download and install the VCE Exam Simulator for further practice. The DP-100 PDF is printable in high quality, allowing you to take it with you on vacations or while traveling. Your updated DP-100 exam files can be accessed anytime from your online account, and you will receive your login credentials immediately after purchase.
