Designing and Implementing a Data Science Solution on Azure
Last Update Feb 14, 2025
Total Questions : 441
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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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