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  • Exam Code: DP-100日本語
  • Exam Name: Designing and Implementing a Data Science Solution on Azure (DP-100日本語版)
  • Updated: Jul 25, 2026
  • Q & A: 528 Questions and Answers
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  • Exam Code: DP-100日本語
  • Exam Name: Designing and Implementing a Data Science Solution on Azure (DP-100日本語版)
  • Updated: Jul 25, 2026
  • Q & A: 528 Questions and Answers
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  • Exam Code: DP-100日本語
  • Exam Name: Designing and Implementing a Data Science Solution on Azure (DP-100日本語版)
  • Updated: Jul 25, 2026
  • Q & A: 528 Questions and Answers
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Efficient study plan

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What Certificate You Will Get by Passing DP-100

DP-100 syllabus includes concepts of machine learning workloads, handling data experiments, optimizing and managing models, and many more. This is an associate-level test that creates a strong base for candidates' future professional development. This Microsoft exam is associated with the Microsoft Certified: Azure Data Scientist Associate certification. This is the only test that one has to ace to become accredited and is considered the best choice as it has no formal prerequisites & allows specialists to validate their proficiency in utilizing Azure Machine Learning Service and many other related solutions.

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

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Exam Details & Skills Measured

Microsoft DP-100 is an associate-level certification exam. It can be taken as a proctored test if this option is available in your country. If it’s not, you can sit for it as an online exam at one of the Pearson VUE testing centers across the world. You should check the official webpage for more information about scheduling this exam.

When you are ready for this test, you have to pay $165 as a registration fee, which applies to a single delivery of the exam. You can take it in English, Korean, Japanese, or Simplified Chinese. Microsoft DP-100 contains 40-60 questions that cover different types, such as drag and drop, multiple choice, active screen, build list, short answer, best answer, and case studies, among others. If you want to pass this test on the first try, you should get 720 points on a scale of 100-1000.

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Microsoft DP-100日本語 Exam Syllabus Topics:

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

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