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Microsoft DP-100 Exam Syllabus

Microsoft DP-100 Exam

Designing and Implementing a Data Science Solution on Azure

Total Questions: 265

What is Included in the Microsoft DP-100 Exam?

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Microsoft DP-100 Exam Overview :

Exam Name Designing and Implementing a Data Science Solution on Azure
Exam Code DP-100
Actual Exam Duration 120 minutes
Expected no. of Questions in Actual Exam 60
Exam Registration Price $165
Official Information https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
See Expected Questions Microsoft DP-100 Expected Questions in Actual Exam
Take Self-Assessment Use Microsoft DP-100 Practice Test to Assess your preparation - Save Time and Reduce Chances of Failure

Microsoft DP-100 Exam Topics :

Section Weight Objectives
Create an Azure Machine Learning workspace 30-35% - 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 30-35% - register and maintain datastores
- create and manage datasets
Manage experiment compute contexts 30-35% - create a compute instance
- determine appropriate compute specifications for a training workload
- create compute targets for experiments and training
Create models by using Azure Machine Learning Designer 25-30% - create a training pipeline by using Azure Machine Learning 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 25-30% - create and run an experiment by using the Azure Machine Learning SDK
- configure run settings for a script
- consume data from a dataset in an experiment by using the Azure Machine Learning SDK
Generate metrics from an experiment run 25-30% - log metrics from an experiment run
- retrieve and view experiment outputs
- use logs to troubleshoot experiment run errors
Automate the model training process 25-30% - create a pipeline by using the SDK
- pass data between steps in a pipeline
- run a pipeline
- monitor pipeline runs
Use Automated ML to create optimal models 20-25% - use the Automated ML interface in Azure Machine Learning studio
- use Automated ML from the Azure Machine Learning SDK
- select 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 tune hyperparameters 20-25% - 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 20-25% - select a model interpreter
- generate feature importance data
Manage models 20-25% - register a trained model
- monitor model usage
- monitor data drift
Create production compute targets 20-25% - consider security for deployed services
- evaluate compute options for deployment
Deploy a model as a service 20-25% - configure deployment settings
- consume a deployed service
- troubleshoot deployment container issues
Create a pipeline for batch inferencing 20-25% - publish a batch inferencing pipeline
- run a batch inferencing pipeline and obtain outputs
Publish a designer pipeline as a web service 20-25% - create a target compute resource
- configure an Inference pipeline
- consume a deployed endpoint

Updates in the Microsoft DP-100 Exam Syllabus:

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