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

  • Exam Code: DP-100J
  • Exam Name: Designing and Implementing a Data Science Solution on Azure (DP-100日本語版)
  • Updated: Aug 07, 2026
  • Q&As: 528 Questions and 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 Exam Reference

Microsoft DP-100日本語 Exam Syllabus Topics:

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

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