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

  • Exam Code: DP-100 Korean
  • Exam Name: Designing and Implementing a Data Science Solution on Azure (DP-100 Korean Version)
  • Updated: Aug 29, 2026     Q & A: 528 Questions and Answers

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How much DP-100 Exam Cost

The price of the DP-100 exam is $165 USD.

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Step 5: Studying with Books

Books is another efficient preparation tool. There are several guides available for the Microsoft DP-100 exam but the most popular are listed below:

  • Designing and Implementing a Data Science Solution on Azure: Prepare for exam DP-100 by Stavros Koureas
  • Designing and Implementing a Data Science Solution on Azure - Exam Prep: Microsoft Azure DP-100 Certification Exam | Most Unique & Latest Questionnaires by VB Dev
  • How I Passed DP-100: Designing and Implementing a Data Science Solution on Azure: Sure Shot Certification Tips by Empirical Matt Publications
  • Learn How to Pass DP-100: Designing and Implementing a Data Science Solution on Azure Exam: Learn at your fingertips by Rodrezil Publications
  • Exam Ref DP-100 Designing and Implementing a Data Science Solution on Azure 1st Edition by Pierstefano Tucci

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

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.

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4. Models Deployment and Consumption (20-25%):

  • Designer pipeline publishing as a web service: The candidates should show their knowledge of target compute resources creation, inference pipelines configuration, and deployed endpoints consuming.
  • Creation of pipelines for batch inferencing: This subject area covers your competence in running batch inferencing pipelines and obtaining outputs as well as publishing batch inferencing pipelines.
  • Production computes targets creation: The test takers should perform their skills in compute options evaluation for deployment and consideration of security for deployed services.
  • Model-as-a-Service deployment: This subtopic will measure the individuals’ expertise in configuring deployment settings, troubleshooting issues with deployment containers, and consuming deployed services.

Microsoft DP-100 Korean Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Explore data and run experiments20-25%- Explore and visualize data
  • 1. Profile and validate data
  • 2. Detect anomalies and outliers
  • 3. Identify features and relationships
- Run experiments
  • 1. Use automated machine learning
  • 2. Define parameters and configurations
  • 3. Track runs with MLflow
  • 4. Configure experiment runs
- Implement pipelines
  • 1. Create and publish pipelines
  • 2. Build reusable components
  • 3. Schedule and monitor pipelines
  • 4. Pass data between steps
Topic 2: Design and prepare a machine learning solution20-25%- Manage data assets
  • 1. Register and manage datastores
  • 2. Create and maintain data assets
  • 3. Select storage services
- Manage compute resources
  • 1. Select environments
  • 2. Attach and monitor compute
  • 3. Create and configure compute targets
- Design a machine learning solution
  • 1. Define compute specifications for workloads
  • 2. Plan model deployment requirements
  • 3. Determine dataset structure and format
  • 4. Select development approach
- Manage Azure Machine Learning workspace
  • 1. Set up Git integration
  • 2. Work with registries
  • 3. Create and configure workspace
  • 4. Use developer tools and CLI
Topic 3: Train and deploy models25-30%- Train models
  • 1. Apply responsible AI principles
  • 2. Run training scripts
  • 3. Use HyperDrive for hyperparameter tuning
  • 4. Configure jobs and environments
- Deploy models
  • 1. Deploy to online endpoints
  • 2. Configure compute and scaling
  • 3. Secure endpoints and manage access
  • 4. Deploy to batch endpoints
- Manage models
  • 1. Package and validate models
  • 2. Register and version models
  • 3. Interpret models and explain predictions
- Monitor and maintain models
  • 1. Update and retrain models
  • 2. Monitor performance and data drift
  • 3. Implement MLOps practices
Topic 4: Optimize language models for AI applications25-30%- Evaluate and improve models
  • 1. Optimize for accuracy and safety
  • 2. Test and evaluate responses
  • 3. Apply responsible generative AI
- Optimize with Retrieval Augmented Generation
  • 1. Create vector stores and indexes
  • 2. Prepare and process data
  • 3. Configure Azure AI Search
- Implement generative AI solutions
  • 1. Build prompt flows
  • 2. Apply prompt engineering
  • 3. Use Azure AI Foundry

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