Microsoft DP-100-KR Questions & Answers - in .pdf
- Total Q&A: 528
- Update: Aug 03, 2026
- Price: $69.99
- Vendor: Microsoft
- Exam Code: DP-100-KR
- Exam Name: Designing and Implementing a Data Science Solution on Azure (DP-100 Korean Version)
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Microsoft DP-100-KR Q&A - Testing Engine
- Total Q&A: 528
- Update: Aug 03, 2026
- Price: $69.99
- Vendor: Microsoft
- Exam Code: DP-100-KR
- Exam Name: Designing and Implementing a Data Science Solution on Azure (DP-100 Korean Version)
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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:
- Create No-Code Predictive Models with Azure Machine Learning
- Build AI Solutions with Azure Machine Learning
- Create Machine Learning Models
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
Testing Process Details of Microsoft DP-100
As for the exam format, there are usually about 40-60 multiple-choice, active screen, best answer, hot area, build lists, labs, mark review and other question styles. You are expected to complete them in 3 hours. The passing score is 700 out of 1000 maximum possible points. The test is available in the following languages: English, Japanese, Korean, and Chinese (Simplified). To schedule your test, you should first pay the registration fee of $165 and go to the Pearson VUE platform to create an account. Once you log in, choose the “Proctored Exams” section and enter ‘DP-100' as the exam code. Follow the prompts to complete registration. At this stage, you can choose to take your test offline or online. Notice, though, that if you don't succeed on the first try, you'll be required to pay the enrollment fee for the next attempt as well.
Microsoft DP-100 Korean Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|
| Topic 1: Design and prepare a machine learning solution | 20-25% | - Design an Azure Machine Learning workspace
- 1. Configure security and access
- 2. Manage compute resources
- 3. Configure workspace resources
- Prepare development environments
- 1. Configure environments
- 2. Use SDKs and notebooks
|
| Topic 2: Deploy and retrain models | 10-15% | - Monitor deployed models
- 1. Track data drift
- 2. Monitor model performance
- Implement retraining pipelines
- 1. Create scheduled retraining workflows
- 2. Manage ML pipelines
|
| Topic 3: Prepare a model for deployment | 20-25% | - Deploy machine learning models
- 1. Deploy batch inference pipelines
- 2. Deploy real-time inference endpoints
- Manage deployment assets
- 1. Register models
- 2. Create inference configurations
|
| Topic 4: Explore data and train models | 35-40% | - Optimize model performance
- 1. Improve accuracy and performance
- 2. Evaluate models
- Run experiments and train models
- 1. Track experiments
- 2. Perform hyperparameter tuning
- 3. Use automated machine learning
- Prepare data for modeling
- 1. Manage datasets and datastores
- 2. Ingest and transform data
|
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