Microsoft Designing and Implementing a Data Science Solution on Azure (DP-100 Korean Version) : DP-100 Korean Exam

  • Exam Code: DP-100-KR
  • Exam Name: Designing and Implementing a Data Science Solution on Azure (DP-100 Korean Version)
  • Updated: Aug 10, 2026
  • Q & A: 528 Questions and Answers

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About Microsoft Designing and Implementing a Data Science Solution on Azure (DP-100 Korean Version) : DP-100 Korean Exam Questions

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What Certificate You Will Get by Passing DP-100

DP-100 syllabus includes concepts of machine learning workloads, handling data experiments, optimizing and managing models, and many more. This is an associate-level test that creates a strong base for candidates' future professional development. This Microsoft exam is associated with the Microsoft Certified: Azure Data Scientist Associate certification. This is the only test that one has to ace to become accredited and is considered the best choice as it has no formal prerequisites & allows specialists to validate their proficiency in utilizing Azure Machine Learning Service and many other related solutions.

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

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DP-100 Exam topics

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  • Perform feature engineering 15-20%
  • Develop models 40-45%
  • Prepare data for modeling 25-30%
  • Define and prepare the development environment 15-20%

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Microsoft DP-100 Korean Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Explore data and train models35-40%- Run experiments and train models
  • 1. Track experiments
  • 2. Perform hyperparameter tuning
  • 3. Use automated machine learning
- Optimize model performance
  • 1. Improve accuracy and performance
  • 2. Evaluate models
- Prepare data for modeling
  • 1. Manage datasets and datastores
  • 2. Ingest and transform data
Topic 2: Prepare a model for deployment20-25%- Manage deployment assets
  • 1. Register models
  • 2. Create inference configurations
- Deploy machine learning models
  • 1. Deploy real-time inference endpoints
  • 2. Deploy batch inference pipelines
Topic 3: Design and prepare a machine learning solution20-25%- Design an Azure Machine Learning workspace
  • 1. Configure workspace resources
  • 2. Manage compute resources
  • 3. Configure security and access
- Prepare development environments
  • 1. Use SDKs and notebooks
  • 2. Configure environments
Topic 4: Deploy and retrain models10-15%- Monitor deployed models
  • 1. Track data drift
  • 2. Monitor model performance
- Implement retraining pipelines
  • 1. Manage ML pipelines
  • 2. Create scheduled retraining workflows

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