Microsoft DP-100日本語 : Designing and Implementing a Data Science Solution on Azure (DP-100日本語版)

DP-100日本語 pass collection

Exam Code: DP-100J

Exam Name: Designing and Implementing a Data Science Solution on Azure (DP-100日本語版)

Updated: Aug 02, 2026

Q & A: 528 Questions and Answers

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

Microsoft DP-100日本語 Exam Syllabus Topics:

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

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