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  • Exam Code: DP-100日本語
  • Exam Name: Designing and Implementing a Data Science Solution on Azure (DP-100日本語版)
  • Updated: Oct 01, 2026
  • Q & A: 528 Questions and Answers
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  • Exam Code: DP-100日本語
  • Exam Name: Designing and Implementing a Data Science Solution on Azure (DP-100日本語版)
  • Updated: Oct 01, 2026
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  • Exam Code: DP-100日本語
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Microsoft DP-100日本語 Exam Syllabus Topics:

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

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