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

DP-100 Korean Exam Questions
  • Exam Code: DP-100-KR
  • Exam Name: Designing and Implementing a Data Science Solution on Azure (DP-100 Korean Version)
  • Updated: Jul 25, 2026
  • Q & A: 528 Questions and Answers
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About Microsoft DP-100 Korean Exam Guide

How to Register For Exam DP-100: Designing and Implementing a Data Science Solution on Azure?

Exam Register Link: https://examregistration.microsoft.com/?locale=en-us&examcode=DP-100&examname=Exam%20DP-100:%20Designing%20and%20Implementing%20a%20Data%20Science%20Solution%20on%20Azure&returnToLearningUrl=https%3A%2F%2Fdocs.microsoft.com%2Flearn%2Fcertifications%2Fexams%2Fdp-100

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

Certification Path

The Microsoft Certified Azure Data Scientist Associate Certification includes only one DP-100 Exam.

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Microsoft DP-100: Requirements

The Microsoft DP-100 exam is created for the individuals who are involved in implementing machine learning techniques. The candidates for this certification test should have an in-depth knowledge of identifying and preparing the development environment, applying scientific data and rigor exploration techniques to acquire actionable insights along with communicating results to the stakeholders. They should also possess the skills in preparing data for modeling and developing models. In addition, they should also be able to apply machine learning techniques to train, deploy and evaluate models to build any AI solutions, which satisfy the business objectives.

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DP-100 Korean exam dumps

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Microsoft DP-100 Korean Exam Syllabus Topics:

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

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