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

  • 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: Aug 10, 2026
  • Q & A: 528 Questions and Answers

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Step 2: Enrolling for the Instructor-Led Training Course

This is the most recommended way of preparing for the Microsoft DP-100 exam. Microsoft offers the official training course under the title “Designing and Implementing a Data Science Solution on Azure” to help the students build a solid knowledge base for the test. The training sessions are delivered to the individuals by the world-class professionals with plenty of experience in the field. So, the candidates get trained accordingly in the best way possible. Another advantage of taking the instructor-led course is that you have an opportunity to ask your questions and receive feedback whenever you face difficulties.

For more info visit:

Microsoft DP-100 Exam Reference

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

Microsoft DP-100日本語 Exam Syllabus Topics:

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

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