Microsoft Designing and Implementing a Data Science Solution on Azure : DP-100 Exam

  • Exam Code: DP-100
  • Exam Name: Designing and Implementing a Data Science Solution on Azure
  • Updated: Aug 04, 2026
  • Q & A: 528 Questions and Answers

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About Microsoft Designing and Implementing a Data Science Solution on Azure : DP-100 Exam Questions

Microsoft DP-100 Practice Test Questions, Microsoft DP-100 Exam Practice Test Questions

The Microsoft DP-100: Designing & Implementing a Data Science Solution on Azure exam evaluates the skills and knowledge of the professionals in a range of technical tasks. These include training models and running experiments, setting up an Azure ML workspace, consuming and deploying models, and managing & optimizing models. This test is the only qualifying exam that leads to the Microsoft Certified: Azure Data Scientist Associate certification.

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The candidates who want to take the Microsoft DP-100 exam are expected to have competence in its objectives. This means that they need to understand the scope of topics covered in this certification test. They are as follows:

1. Azure ML Workspace Set-Up (30-35%):

  • Experiment compute contexts management: The test takers must be able to create compute instances and compute targets for training and experiments. They have to know how to establish suitable compute specifications for training workloads.
  • Azure ML workspace creation: This area focuses on the students’ skills in creating Azure ML workspace, operating workspaces by using Azure ML studio, and configuring workspace settings.
  • Data objects management within Azure ML workspace: The applicants should have competence in the creation and management of datasets, as well as maintenance and registration of datastores.

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4. Models Deployment and Consumption (20-25%):

  • Designer pipeline publishing as a web service: The candidates should show their knowledge of target compute resources creation, inference pipelines configuration, and deployed endpoints consuming.
  • Production computes targets creation: The test takers should perform their skills in compute options evaluation for deployment and consideration of security for deployed services.
  • Model-as-a-Service deployment: This subtopic will measure the individuals’ expertise in configuring deployment settings, troubleshooting issues with deployment containers, and consuming deployed services.
  • Creation of pipelines for batch inferencing: This subject area covers your competence in running batch inferencing pipelines and obtaining outputs as well as publishing batch inferencing pipelines.

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

Microsoft DP-100 Exam Syllabus Topics:

SectionWeightObjectives
Prepare a model for deployment20-25%- Deploy machine learning models
  • 1. Deploy batch inference pipelines
  • 2. Deploy real-time inference endpoints
- Manage deployment assets
  • 1. Register models
  • 2. Create inference configurations
Explore data and train models35-40%- Prepare data for modeling
  • 1. Manage datasets and datastores
  • 2. Ingest and transform data
- Optimize model performance
  • 1. Improve accuracy and performance
  • 2. Evaluate models
- Run experiments and train models
  • 1. Perform hyperparameter tuning
  • 2. Track experiments
  • 3. Use automated machine learning
Design and prepare a machine learning solution20-25%- Design an Azure Machine Learning workspace
  • 1. Configure security and access
  • 2. Configure workspace resources
  • 3. Manage compute resources
- Prepare development environments
  • 1. Use SDKs and notebooks
  • 2. Configure environments
Deploy and retrain models10-15%- Monitor deployed models
  • 1. Track data drift
  • 2. Monitor model performance
- Implement retraining pipelines
  • 1. Create scheduled retraining workflows
  • 2. Manage ML pipelines

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