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

  • Exam Code: DP-100J
  • Exam Name: Designing and Implementing a Data Science Solution on Azure (DP-100日本語版)
  • Updated: Sep 04, 2026
  • Q&As: 528 Questions and Answers

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What is the duration of the DP-100 Exam

  • Length of Examination: 150 minutes
  • Number of Questions: 45-60
  • Passing Score: 700/1000
  • Format: Multiple choices, multiple answers

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What Next after This Certification?

The certification obtained from passing DP-100 exam is proof of intermediate skills in data science since it is within the associate hierarchy. Apart from applying for and getting a job in a preferred company, you can also expand your skills in an area of interest. With the role-based scheme, there are expert certifications awaiting you to earn. They include the Microsoft Certified: Azure Solutions Architect Expert and the Microsoft Certified: Azure DevOps Engineer Expert.

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

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Microsoft DP-100日本語 Exam Syllabus Topics:

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

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