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

  • Exam Code: DP-100J
  • Exam Name: Designing and Implementing a Data Science Solution on Azure (DP-100日本語版)
  • Updated: Jul 18, 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

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The candidates who want to take the Microsoft DP-100 exam are expected to have competence in its objectives. This means that they need to understand the scope of topics covered in this certification test. They are as follows:

1. Azure ML Workspace Set-Up (30-35%):

  • Data objects management within Azure ML workspace: The applicants should have competence in the creation and management of datasets, as well as maintenance and registration of datastores.
  • Azure ML workspace creation: This area focuses on the students’ skills in creating Azure ML workspace, operating workspaces by using Azure ML studio, and configuring workspace settings.
  • Experiment compute contexts management: The test takers must be able to create compute instances and compute targets for training and experiments. They have to know how to establish suitable compute specifications for training workloads.

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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- Manage compute and data assets
- Plan and configure Azure Machine Learning workspace
- Select appropriate Azure services for machine learning workloads
Topic 4: Deploy and consume models- Deploy models to endpoints
- Monitor deployed models and endpoints
Topic 5: Optimize and manage models- Improve model performance
- Track experiments and manage model lifecycle

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