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

  • Exam Code: DP-100J
  • Exam Name: Designing and Implementing a Data Science Solution on Azure (DP-100日本語版)
  • Updated: Aug 01, 2026
  • Q & A: 528 Questions and Answers

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Microsoft DP-100 Practice Test Questions, Microsoft DP-100 Exam Practice Test Questions

The Microsoft DP-100: Designing & Implementing a Data Science Solution on Azure exam evaluates the skills and knowledge of the professionals in a range of technical tasks. These include training models and running experiments, setting up an Azure ML workspace, consuming and deploying models, and managing & optimizing models. This test is the only qualifying exam that leads to the Microsoft Certified: Azure Data Scientist Associate certification.

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

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.

Microsoft DP-100日本語 Exam Syllabus Topics:

SectionObjectives
Topic 1: Train machine learning models- Train models using Azure Machine Learning
- Tune hyperparameters and evaluate models
Topic 2: Explore and analyze data- Perform exploratory data analysis
- Ingest and prepare data for modeling
Topic 3: Design and prepare a machine learning solution- Select appropriate Azure services for machine learning workloads
- Plan and configure Azure Machine Learning workspace
- Manage compute and data assets
Topic 4: Optimize and manage models- Track experiments and manage model lifecycle
- Improve model performance
Topic 5: Deploy and consume models- Monitor deployed models and endpoints
- Deploy models to endpoints

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