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

  • Exam Code: DP-100-KR
  • Exam Name: Designing and Implementing a Data Science Solution on Azure (DP-100 Korean Version)
  • Updated: Sep 07, 2026
  • Q&As: 528 Questions and Answers

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The candidates who want to take the Microsoft DP-100 exam are expected to have competence in its objectives. This means that they need to understand the scope of topics covered in this certification test. They are as follows:

1. Azure ML Workspace Set-Up (30-35%):

  • Azure ML workspace creation: This area focuses on the students’ skills in creating Azure ML workspace, operating workspaces by using Azure ML studio, and configuring workspace settings.
  • Experiment compute contexts management: The test takers must be able to create compute instances and compute targets for training and experiments. They have to know how to establish suitable compute specifications for training workloads.
  • Data objects management within Azure ML workspace: The applicants should have competence in the creation and management of datasets, as well as maintenance and registration of datastores.

Microsoft DP-100 certification exam is a highly regarded certification in the field of data science. DP-100-KR exam is designed to test the candidate's expertise in designing and implementing data science solutions on Microsoft Azure. Candidates who pass the exam will be able to demonstrate their ability to design and implement data science solutions on Microsoft Azure, which can open up new career opportunities in the field of data science.

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

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Microsoft DP-100 Korean Exam Syllabus Topics:

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

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