Microsoft DP-100 Korean Exam Details & Actual Exam Questions
- Exam Code/Number: DP-100 Korean
- Exam Name/Title: Designing and Implementing a Data Science Solution on Azure (DP-100 Korean Version)
- Certification Provider: Microsoft
- Corresponding Certification: Microsoft Azure
- Exam Questions: 528
- Updated On: Aug,22 2026
- Certification Level: Associate
Microsoft Designing and Implementing a Data Science Solution on Azure (DP-100 Korean Version) Exam Questions
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Microsoft DP-100 Korean Exam Overview:
| Certification Vendor: | Microsoft |
| Exam Name: | Designing and Implementing a Data Science Solution on Azure |
| Exam Number: | DP-100 |
| Exam Duration: | 100-120 |
| Exam Format: | Multiple choice, Multiple response, Case studies, Drag and drop, Hands-on lab tasks |
| Related Certifications: | Microsoft Certified: Azure Data Scientist Associate |
| Real Exam Qty: | 40-60 |
| Exam Price: | USD 165 (varies by region) |
| Passing Score: | 700/1000 |
| Available Languages: | English, Japanese, Korean, Simplified Chinese, French, German, Spanish, Portuguese (Brazil), Russian |
| Certificate Validity Period: | 1 year (renewable through Microsoft certification renewal assessment) |
| Recommended Training: | Azure Machine Learning Documentation Microsoft Learn - DP-100 Learning Path |
| Exam Registration: | Official Microsoft Certification Page Pearson VUE Registration |
| Sample Questions: | Microsoft DP-100 Korean Sample Questions |
| Exam Way: | Online proctored or in-person at authorized testing centers (Pearson VUE) |
| Pre Condition: | No mandatory prerequisites. Recommended familiarity with Python, machine learning concepts, and Azure fundamentals. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/azure-data-scientist/ |
Microsoft DP-100 Korean Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Optimize and manage models | - Track experiments and manage model lifecycle - Improve model performance |
| Explore and analyze data | - Perform exploratory data analysis - Ingest and prepare data for modeling |
| Deploy and consume models | - Monitor deployed models and endpoints - Deploy models to endpoints |
| Train machine learning models | - Train models using Azure Machine Learning - Tune hyperparameters and evaluate models |
| Design and prepare a machine learning solution | - Select appropriate Azure services for machine learning workloads - Manage compute and data assets - Plan and configure Azure Machine Learning workspace |
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