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

  • Exam Code: Microsoft
  • Exam Name: Designing and Implementing a Data Science Solution on Azure (DP-100 Korean Version)
  • Updated: Aug 01, 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) : Microsoft Exam

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Microsoft DP-100 Exam Reference

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

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2. Train Models & Run Experiments (25-30%):
  • Model training process automation: The individuals need the relevant skills in running pipelines, passing data within steps in pipelines, monitoring pipeline runs, and creating pipelines with the use of SDK.
  • Training scripts run within Azure ML workspaces: The students should have the expertise in creating and running experiments utilizing Azure ML SDK as well configuring run settings for the scripts. This subject area also requires their skills in data consumption from datasets for an experiment using Azure ML SDK.
  • Models creation with Azure ML Designer: This domain covers the examinees’ skills in using custom code modules within the design and using designer modules for the definition of pipeline data flows. It also requires one’s competence in ingesting data within designer pipelines and creating training pipelines utilizing ML Designer.
  • Metrics generation from experiment runs: The candidates must be able to use logs for troubleshooting errors in experiment runs, log metrics from experiment run, and view and retrieve experiment outputs.
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The benefit in Obtaining the DP-100 Exam Certification
  • When Candidates applying for a job or looking to promotion in their current position, a Microsoft Certified Azure Data Scientist Associate certification in the field in which Candidates are applying will put you at the top of the list and make them a desirable candidate for employers.
  • Candidates will get in-depth knowledge by completing the courses along with the access to revision materials for 6 months upon completion means they will have a wider skill set when it comes to the various technologies and systems than an uncertified professional. Certified Professional in this particular skill set is 74% more efficient when it comes to completing their tasks in a timely well-executed manner.
  • Becoming Microsoft Certified Azure Data Scientist Associate means one thing you are worth more to the company and therefore more to yourself in the form of an upgraded pay package. On average a Microsoft Certified Azure Data Scientist Associate member of staff is estimated to be worth 30% more to a company than their uncertified professionals.
  • After completion of Microsoft Certified Azure Data Scientist Associate Certification candidates receive official confirmation from Microsoft that you are now fully certified in their chosen field. This can be now added to their CV, cover letters and job applications.
  • Organization owners invest a lot in their employees when it comes to their training with the goal of making them quicker, more efficient, and more knowledgeable about their role. Certified Professional will reduce the time he spends on tasks, meaning he can get more done this could help reduce company downtime when repairing faults on a system or fixing hardware problems.
Microsoft DP-100 Korean Exam Syllabus Topics:
SectionWeightObjectives
Optimize language models for AI applications25-30%- Implement generative AI solutions
  • 1. Build prompt flows
  • 2. Apply prompt engineering
  • 3. Use Azure AI Foundry
- Optimize with Retrieval Augmented Generation
  • 1. Configure Azure AI Search
  • 2. Create vector stores and indexes
  • 3. Prepare and process data
- Evaluate and improve models
  • 1. Optimize for accuracy and safety
  • 2. Test and evaluate responses
  • 3. Apply responsible generative AI
Explore data and run experiments20-25%- Implement pipelines
  • 1. Schedule and monitor pipelines
  • 2. Pass data between steps
  • 3. Create and publish pipelines
  • 4. Build reusable components
- Run experiments
  • 1. Configure experiment runs
  • 2. Define parameters and configurations
  • 3. Track runs with MLflow
  • 4. Use automated machine learning
- Explore and visualize data
  • 1. Detect anomalies and outliers
  • 2. Profile and validate data
  • 3. Identify features and relationships
Design and prepare a machine learning solution20-25%- Manage compute resources
  • 1. Select environments
  • 2. Attach and monitor compute
  • 3. Create and configure compute targets
- Design a machine learning solution
  • 1. Select development approach
  • 2. Define compute specifications for workloads
  • 3. Plan model deployment requirements
  • 4. Determine dataset structure and format
- Manage data assets
  • 1. Create and maintain data assets
  • 2. Register and manage datastores
  • 3. Select storage services
- Manage Azure Machine Learning workspace
  • 1. Use developer tools and CLI
  • 2. Create and configure workspace
  • 3. Set up Git integration
  • 4. Work with registries
Train and deploy models25-30%- Monitor and maintain models
  • 1. Implement MLOps practices
  • 2. Monitor performance and data drift
  • 3. Update and retrain models
- Train models
  • 1. Configure jobs and environments
  • 2. Use HyperDrive for hyperparameter tuning
  • 3. Apply responsible AI principles
  • 4. Run training scripts
- Manage models
  • 1. Register and version models
  • 2. Interpret models and explain predictions
  • 3. Package and validate models
- Deploy models
  • 1. Deploy to online endpoints
  • 2. Secure endpoints and manage access
  • 3. Configure compute and scaling
  • 4. Deploy to batch endpoints

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