Exam Name: Designing and Implementing a Data Science Solution on Azure (DP-100日本語版)
Certification Provider: Microsoft
Corresponding Certification: Microsoft Azure
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Updated: Sep 06, 2026
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3. Models Management and Optimization (20-25%):
- Usage of model explainers for the interpretation of models: The learners have to demonstrate their competence in choosing model interpreters and generating the features of important data.
- Usage of hyperdrive for the tuning of hyperparameters: This domain will evaluate the ability of the applicants to define search space, primary metrics, and early termination alternatives. It also expects their skills in sampling techniques selection and model discovery that require optimal hyper-parameter values.
- Models management: This objective focuses on trained model registration and monitoring of data drift and model usage.
- Usage of Automated Machine Learning for the creation of optimal models: This section requires your skills in retrieving the best models and getting data for Automated ML runs. It also covers competence in defining primary metrics, selecting pre-processing alternatives, and determining the algorithms to be searched. The candidates should be able to use the Automated Machine Learning from Azure ML SDK as well as Automated ML interface within Azure ML studios.
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DP-100 Exam Target Audience
Candidates with permission to take DP-100 exam are those aspiring to become Azure data scientists and have formed huge knowledge in both data science and machine learning workloads based on Azure.
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
Microsoft DP-100日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Explore data and train models | 35-40% | - Optimize model performance
|
| Topic 2: Deploy and retrain models | 10-15% | - Implement retraining pipelines
|
| Topic 3: Prepare a model for deployment | 20-25% | - Deploy machine learning models
|
| Topic 4: Design and prepare a machine learning solution | 20-25% | - Prepare development environments
|
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