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 testking pdf

Exam Code: DP-100J

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

Updated: Sep 09, 2026

Q & A: 528 Questions and Answers

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

DP-100 Exam Target Audience

Candidates with permission to take DP-100 exam are those aspiring to become Azure data scientists and have formed huge knowledge in both data science and machine learning workloads based on Azure.

Microsoft DP-100日本語 Exam Syllabus Topics:

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

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