Microsoft DP-100 Exam Topics

Microsoft DP-100 Exam Overview :

Exam Name:	Designing and Implementing a Data Science Solution on Azure
Exam Code:	DP-100
Certifications:	Microsoft Azure Data Scientist Associate Certification
Actual Exam Duration:	120 minutes
Expected no. of Questions in Actual Exam:	60
Exam Registration Price:	$165
See Expected Questions:	Microsoft DP-100 Expected Questions in Actual Exam

Microsoft DP-100 Exam Objectives :

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
Official Information		https://www.microsoft.com/en-us/learning/exam-dp-100.aspx

Updates in the Microsoft DP-100 Exam Topics:

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