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

  • Exam Code: DP-100 Korean
  • Exam Name: Designing and Implementing a Data Science Solution on Azure (DP-100 Korean Version)
  • Updated: Aug 01, 2026   Q&As: 528 Questions and Answers

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About Microsoft DP-100 Korean Exam

Career Opportunities & Salary Outlook

The demand for the certified professionals is on the increase. As an Azure Data Scientist, you can explore many employment opportunities. So, if you want to boost your career potential, pursuing the Microsoft Certified: Azure Data Scientist Associate certification is the right step. To get this certificate, you have to pass the qualifying exam, which is Microsoft DP-100. With this associate-level certification, you can get the positions, such as a Data Scientist, a Senior Data Scientist, a Data Science Manager, and a Data Science Director. The average salary for these job roles is $135,000 per annum. With advanced experience, the specialists can earn up to $170,000 per year.

Conclusion

Data science is a lucrative career option and you can explore it without any hassle if you’ve managed to taste success in the Microsoft DP-100 exam, which is known to create a skilled workforce of Azure data scientists. However, a great outcome in such a test will only come if the aspirant is referring to the updated and recent study resources like the official courses provided by the exam vendor itself.

3. Models Management and Optimization (20-25%):
  • Usage of model explainers for the interpretation of models: The learners have to demonstrate their competence in choosing model interpreters and generating the features of important data.
  • Usage of Automated Machine Learning for the creation of optimal models: This section requires your skills in retrieving the best models and getting data for Automated ML runs. It also covers competence in defining primary metrics, selecting pre-processing alternatives, and determining the algorithms to be searched. The candidates should be able to use the Automated Machine Learning from Azure ML SDK as well as Automated ML interface within Azure ML studios.
  • Models management: This objective focuses on trained model registration and monitoring of data drift and model usage.
  • Usage of hyperdrive for the tuning of hyperparameters: This domain will evaluate the ability of the applicants to define search space, primary metrics, and early termination alternatives. It also expects their skills in sampling techniques selection and model discovery that require optimal hyper-parameter values.

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

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Microsoft DP-100 Korean Exam Syllabus Topics:
SectionWeightObjectives
Optimize language models for AI applications25-30%- Implement generative AI solutions
  • 1. Build prompt flows
  • 2. Use Azure AI Foundry
  • 3. Apply prompt engineering
- Evaluate and improve models
  • 1. Optimize for accuracy and safety
  • 2. Apply responsible generative AI
  • 3. Test and evaluate responses
- Optimize with Retrieval Augmented Generation
  • 1. Create vector stores and indexes
  • 2. Prepare and process data
  • 3. Configure Azure AI Search
Design and prepare a machine learning solution20-25%- Design a machine learning solution
  • 1. Plan model deployment requirements
  • 2. Select development approach
  • 3. Determine dataset structure and format
  • 4. Define compute specifications for workloads
- Manage data assets
  • 1. Select storage services
  • 2. Register and manage datastores
  • 3. Create and maintain data assets
- Manage Azure Machine Learning workspace
  • 1. Work with registries
  • 2. Set up Git integration
  • 3. Create and configure workspace
  • 4. Use developer tools and CLI
- Manage compute resources
  • 1. Attach and monitor compute
  • 2. Create and configure compute targets
  • 3. Select environments
Explore data and run experiments20-25%- Run experiments
  • 1. Track runs with MLflow
  • 2. Use automated machine learning
  • 3. Define parameters and configurations
  • 4. Configure experiment runs
- Implement pipelines
  • 1. Schedule and monitor pipelines
  • 2. Pass data between steps
  • 3. Create and publish pipelines
  • 4. Build reusable components
- Explore and visualize data
  • 1. Profile and validate data
  • 2. Detect anomalies and outliers
  • 3. Identify features and relationships
Train and deploy models25-30%- Deploy models
  • 1. Deploy to online endpoints
  • 2. Deploy to batch endpoints
  • 3. Secure endpoints and manage access
  • 4. Configure compute and scaling
- Train models
  • 1. Apply responsible AI principles
  • 2. Run training scripts
  • 3. Configure jobs and environments
  • 4. Use HyperDrive for hyperparameter tuning
- Manage models
  • 1. Package and validate models
  • 2. Interpret models and explain predictions
  • 3. Register and version models
- Monitor and maintain models
  • 1. Monitor performance and data drift
  • 2. Update and retrain models
  • 3. Implement MLOps practices

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