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Updated: Aug 01, 2026

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

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
Exam Duration:100 minutes
Related Certifications:Microsoft Certified: Azure Data Engineer Associate
Microsoft Certified: Azure AI Engineer Associate
Real Exam Qty:40-60
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
Exam Format:Multiple choice, Multiple select, Drag and drop, Case studies, Yes/No
Passing Score:700
Exam Price:$165 USD
Recommended Training:Microsoft Learn Learning Path
Course DP-100T01-A: Designing and Implementing a Data Science Solution on Azure
Exam Registration:Pearson VUE Scheduling
Microsoft Learn Registration
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

4. Models Deployment and Consumption (20-25%):

  • Creation of pipelines for batch inferencing: This subject area covers your competence in running batch inferencing pipelines and obtaining outputs as well as publishing batch inferencing pipelines.
  • Production computes targets creation: The test takers should perform their skills in compute options evaluation for deployment and consideration of security for deployed services.
  • Model-as-a-Service deployment: This subtopic will measure the individuals’ expertise in configuring deployment settings, troubleshooting issues with deployment containers, and consuming deployed services.
  • Designer pipeline publishing as a web service: The candidates should show their knowledge of target compute resources creation, inference pipelines configuration, and deployed endpoints consuming.

Microsoft DP-100日本語 Exam Syllabus Topics:

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

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