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

"Designing and Implementing a Data Science Solution on Azure (DP-100日本語版)", also known as DP-100 exam, is a Microsoft Certification. With the complete collection of questions and answers, BraindumpsPass has assembled to take you through 528 Q&As to your DP-100 Exam preparation. In the DP-100 exam resources, you will cover every field and category in Microsoft Azure Certification helping to ready you for your successful Microsoft Certification.

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  • Exam Code: DP-100
  • Exam Name: Designing and Implementing a Data Science Solution on Azure (DP-100日本語版)
  • Certification Provider: Microsoft
  • Corresponding Certification: Microsoft Azure
  • Updated: Jul 20, 2026
  • No. of Questions: 528 Questions & Answers with Testing Engine
  • Download Limit: Unlimited

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Step 3: Taking Online Learning Paths

In addition, Microsoft recommends that the candidates for the DP-100 exam take online courses to adequately prepare for their test. You can find the following learning paths on the official certification webpage:

  • Build AI Solutions with Azure Machine Learning
  • Create No-Code Predictive Models with Azure Machine Learning
  • Create Machine Learning Models

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Basic Exam Traits

The Microsoft DP-100 is an associate-level job role-based exam. Its structure is the same as any other exam falling in this category. As per the standard test format, DP-100 exam is likely to contain 40-60 exam questions. As far as question format is concerned, Microsoft doesn’t follow a set pattern. The exam is likely to cover questions based on the MCQ pattern. However, the odds of including items based on other patterns like case studies and best answers are also high. What’s more, there is no exact passing score as there is no fixed number of questions and it might change as per the final number of tasks. Nonetheless, a test-taker must secure 70% passing marks to be called successful in the official exam. Currently, this test can be taken in English, Japanese, Chinese (Simplified), and Korean worldwide. The standard exam fee is $165 and is likely to get changed as per the location of the test-taker.

How to Register For Exam DP-100: Designing and Implementing a Data Science Solution on Azure?

Exam Register Link: https://examregistration.microsoft.com/?locale=en-us&examcode=DP-100&examname=Exam%20DP-100:%20Designing%20and%20Implementing%20a%20Data%20Science%20Solution%20on%20Azure&returnToLearningUrl=https%3A%2F%2Fdocs.microsoft.com%2Flearn%2Fcertifications%2Fexams%2Fdp-100

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

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Microsoft DP-100日本語 Exam Syllabus Topics:

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

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