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Last Updated: Sep 12, 2026

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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%):

  • 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.
  • 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.

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

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Microsoft DP-100 Korean Exam Syllabus Topics:

SectionWeightObjectives
Explore data and run experiments20-25%- Implement pipelines
  • 1. Pass data between steps
  • 2. Create and publish pipelines
  • 3. Build reusable components
  • 4. Schedule and monitor pipelines
- Run experiments
  • 1. Define parameters and configurations
  • 2. Track runs with MLflow
  • 3. Use automated machine learning
  • 4. Configure experiment runs
- Explore and visualize data
  • 1. Profile and validate data
  • 2. Identify features and relationships
  • 3. Detect anomalies and outliers
Optimize language models for AI applications25-30%- Evaluate and improve models
  • 1. Apply responsible generative AI
  • 2. Test and evaluate responses
  • 3. Optimize for accuracy and safety
- Optimize with Retrieval Augmented Generation
  • 1. Prepare and process data
  • 2. Configure Azure AI Search
  • 3. Create vector stores and indexes
- Implement generative AI solutions
  • 1. Build prompt flows
  • 2. Use Azure AI Foundry
  • 3. Apply prompt engineering
Design and prepare a machine learning solution20-25%- Manage data assets
  • 1. Register and manage datastores
  • 2. Select storage services
  • 3. Create and maintain data assets
- Manage Azure Machine Learning workspace
  • 1. Set up Git integration
  • 2. Work with registries
  • 3. Create and configure workspace
  • 4. Use developer tools and CLI
- Manage compute resources
  • 1. Select environments
  • 2. Create and configure compute targets
  • 3. Attach and monitor compute
- Design a machine learning solution
  • 1. Select development approach
  • 2. Plan model deployment requirements
  • 3. Determine dataset structure and format
  • 4. Define compute specifications for workloads
Train and deploy models25-30%- Train models
  • 1. Use HyperDrive for hyperparameter tuning
  • 2. Configure jobs and environments
  • 3. Apply responsible AI principles
  • 4. Run training scripts
- Deploy models
  • 1. Configure compute and scaling
  • 2. Deploy to batch endpoints
  • 3. Deploy to online endpoints
  • 4. Secure endpoints and manage access
- Monitor and maintain models
  • 1. Implement MLOps practices
  • 2. Update and retrain models
  • 3. Monitor performance and data drift
- Manage models
  • 1. Register and version models
  • 2. Package and validate models
  • 3. Interpret models and explain predictions

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