Microsoft DP-100日本語 Exam Details & Actual Exam Questions

  • Exam Code/Number: DP-100日本語
  • Exam Name/Title: Designing and Implementing a Data Science Solution on Azure (DP-100日本語版)
  • Certification Provider: Microsoft
  • Corresponding Certification: Microsoft Azure
  • Exam Questions: 528
  • Updated On: Jul,15 2026
  • Certification Level: Associate

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

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Microsoft DP-100日本語 Exam Overview:

Certification Vendor:Microsoft
Exam Name:Designing and Implementing a Data Science Solution on Azure
Exam Number:DP-100
Available Languages:English, Japanese, Chinese (Simplified), Korean, German, Chinese (Traditional), French, Spanish, Portuguese (Brazil), Italian, Russian, Arabic (Saudi Arabia), Indonesian (Indonesia)
Certificate Validity Period:1 year
Related Certifications:Microsoft Certified: Azure Data Engineer Associate
Microsoft Certified: Azure AI Engineer Associate
Exam Format:Multiple choice, Multiple select, Drag and drop, Case studies, Yes/No
Real Exam Qty:40-60
Passing Score:700
Exam Price:$165 USD
Exam Duration:100 minutes
Sample Questions:Microsoft DP-100日本語 Sample Questions
Exam Way:Online proctored or onsite at Pearson VUE test centers
Pre Condition:No mandatory prerequisites; recommended knowledge: Azure fundamentals, Python programming, data science concepts, machine learning frameworks (Scikit-learn, PyTorch, Tensorflow)
Official Syllabus URL:https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/dp-100

Microsoft DP-100日本語 Exam Syllabus Topics:

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


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