Designing and Implementing a Data Science Solution on Azure
Last Update Nov 30, 2025
Total Questions : 516
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| 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 |
| 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 |
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TESTED 30 Nov 2025
