Microsoft 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: Oct 26, 2026
  • Q&As: 528 Questions and Answers

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Microsoft DP-100: Exam Details

Microsoft doesn’t provide detailed information regarding its exams, including the DP-100 certification test. The total number of questions in exam is not published on the official website, but the candidates can expect to encounter about 40-60 items in their delivery of their test. There is also a high probability that they will face scenario-based questions with single-answer and multiple-answer options. The duration is all about 120 minutes. The exam is available in several languages, including Japanese, English, Simplified Chinese, and Korean.

The Microsoft DP-100 exam can be taken at any nearest Pearson VUE testing center or online as a proctored option. The cost for the exam is $165 in the United States but the price is usually different in other countries due to taxation. To find out the actual pricing for your country, simply check the official webpage.

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Languages: English, Japanese, Chinese (Simplified), Korean

Retirement date: none

This exam measures your ability to accomplish the following technical tasks: manage Azure resources for machine learning; run experiments and train models; deploy and operationalize machine learning solutions; and implement responsible machine learning.

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

Microsoft DP-100 Korean Exam Syllabus Topics:

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

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

Question #1

참고: 이 문제는 동일한 시나리오를 제시하는 일련의 문제 중 하나입니다. 각 문제에는 제시된 목표를 달성할 수 있는 고유한 해결책이 포함되어 있습니다. 일부 문제 세트에는 정답이 두 개 이상일 수 있으며, 정답이 없는 문제 세트도 있습니다.
이 섹션에서 질문에 답변한 후에는 해당 질문으로 돌아갈 수 없습니다. 따라서 이 질문들은 검토 화면에 나타나지 않습니다.
Azure Machine Learning 실험을 실행하기 위해 Python 스크립트를 사용할 계획입니다. 이 스크립트는 실험 실행 컨텍스트에 대한 참조를 생성하고, 파일에서 데이터를 로드하고, 레이블 열에 대한 고유 값 집합을 식별하고, 실험 실행을 완료합니다.

실험에서는 실행 후 검토할 수 있도록 데이터에 고유 레이블을 기록하여 측정 기준으로 활용해야 합니다.
주석에 표시된 지점에 스크립트에 고유 레이블 값을 실행 메트릭으로 기록하는 코드를 추가해야 합니다.
해결 방법: 주석을 다음 코드로 바꾸세요.
run.log_list('레이블 값', label_vals)
이 해결책은 목표를 달성합니까?

  • A. 예
  • B. 아니요
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

Explanation: Only visible for VCEPrep members. You can sign-up / login (it's free).

Question #2

STANDARD_D1 가상 머신 이미지를 사용하여 ComputeOne이라는 이름의 Azure Machine Learning 컴퓨팅 대상을 생성합니다.
Azure Machine Learning 작업 영역을 참조하는 was라는 이름의 Python 변수를 정의합니다. 다음 Python 코드를 실행합니다.

다음 각 문장에 대해, 문장이 사실이면 '예'를 선택하고, 그렇지 않으면 '아니요'를 선택하십시오.
참고: 정답 하나당 1점입니다.

Reveal Solution  Discussion  0

Correct Answer:


Explanation:

Box 1:Yes
ComputeTargetException class: An exception related to failures when creating, interacting with, or configuring a compute target. This exception is commonly raised for failures attaching a compute target, missing headers, and unsupported configuration values.
Create(workspace, name, provisioning_configuration)
Provision a Compute object by specifying a compute type and related configuration.
This method creates a new compute target rather than attaching an existing one.
Box 2: Yes
Box 3: No
The line before print( ' Step1 ' ) will fail.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.compute.computetarget

Question #3

Workspaces라는 이름의 Azure Machine Learning 작업 영역이 있습니다.
Workspace1의 Automated ML을 사용하여 이미지 객체 감지 모델을 학습시킬 계획입니다.
이미지 객체 감지 작업을 시작하려면 제공된 Azure Machine Learning Python SDK v2 코드를 완료해야 합니다.
코드를 어떻게 완성해야 할까요? 정답을 선택하려면 답변란에서 적절한 옵션을 고르세요.
참고: 정답 하나당 1점입니다.

Reveal Solution  Discussion  0

Correct Answer:


Explanation:

Question #4

Azure Machine Learning Studio를 사용하여 대규모 데이터 저장소에서 데이터를 가져오고 있습니다.
시스템 클럭을 기반으로 하는 난수 샘플링 시드를 사용하여 테스트 목적으로 데이터의 하위 집합을 생성해야 합니다.
실험에 파티션 및 샘플 모듈을 추가합니다.
모듈에 대한 속성을 선택해야 합니다.
어떤 값을 선택해야 할까요? 답변하려면 답변란에서 적절한 옵션을 선택하세요.
참고: 정답 하나당 1점입니다.

Reveal Solution  Discussion  0

Correct Answer:


Explanation:

Box 1: Sampling
Create a sample of data
This option supports simple random sampling or stratified random sampling. This is useful if you want to create a smaller representative sample dataset for testing.
1. Add the Partition and Sample module to your experiment in Studio, and connect the dataset.
2. Partition or sample mode: Set this to Sampling.
3. Rate of sampling. See box 2 below.
Box 2: 0
3. Rate of sampling. Random seed for sampling: Optionally, type an integer to use as a seed value.
This option is important if you want the rows to be divided the same way every time. The default value is 0, meaning that a starting seed is generated based on the system clock. This can lead to slightly different results each time you run the experiment.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/partition-and-sample

Question #5

WS1이라는 이름의 Azure Machine Learning 작업 영역이 있습니다.
WS1에 등록할 MLflow 모델을 평가하기 위해 Responsible AI 대시보드를 사용할 계획입니다.
MLflow 모델을 등록하는 데 사용할 라이브러리를 확인해야 합니다.
어떤 라이브러리를 사용해야 할까요?

  • A. PyTorch
  • B. 텐서플로우
  • C. mlpy
  • D. scikit-learn
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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