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

DP-100 Korean real exams

Exam Code: DP-100-KR

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

Updated: Oct 13, 2026

Q & A: 528 Questions and Answers

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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.

The Microsoft DP-100 test helps candidates check their machine learning knowledge and skills and prove themselves as qualified data scientists.

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

Microsoft DP-100 Korean Exam Syllabus Topics:

SectionObjectives
Topic 1: Design and prepare a machine learning solution- Plan and configure Azure Machine Learning workspace
- Select appropriate Azure services for machine learning workloads
- Manage compute and data assets
Topic 2: Deploy and consume models- Monitor deployed models and endpoints
- Deploy models to endpoints
Topic 3: Explore and analyze data- Ingest and prepare data for modeling
- Perform exploratory data analysis
Topic 4: Optimize and manage models- Track experiments and manage model lifecycle
- Improve model performance
Topic 5: Train machine learning models- Tune hyperparameters and evaluate models
- Train models using Azure Machine Learning

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

Question #1

Azure Machine Learning 작업 영역이 있습니다.
Azure Machine Learning Python SDK v2를 사용하여 이미지 분류 모델을 학습시키는 파이프라인 구성 요소를 정의하려고 합니다. 구성 요소의 실행 로직은 modeljrain.py 파일의 train() 함수에 포함되어 있습니다.
필요한 모든 라이브러리를 가져오는 코드를 작성하고, 해당 코드를 model_train.py가 있는 동일한 폴더에 train_component.py라는 이름으로 저장합니다.
train_component.py 파일의 나머지 코드를 완성해야 합니다.
코드를 어떻게 완성해야 할까요? 정답을 선택하려면 답변란에서 적절한 옵션을 고르세요.
참고: 정답 하나당 1점입니다.

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Correct Answer:


Explanation:

Question #2

Azure Machine Learning 작업 영역을 관리하고 있습니다. 서버리스 컴퓨팅으로 구성된 학습 작업을 설계했는데, 이 서버리스 컴퓨팅에는 특정 인스턴스 유형과 개수가 필요합니다. Azure Machine Learning Python SDK v2를 사용하여 서버리스 컴퓨팅을 구성해야 합니다. 어떻게 해야 할까요?

  • A. ResourceConfiguration 클래스를 초기화하고 지정합니다.
  • B. 티어 매개변수를 전용 VM으로 구성합니다.
  • C. 크기와 유형 지정을 사용하여 AmICompute 클래스를 초기화합니다.
  • D. 명령 작업의 compute 매개변수를 사용하여 컴퓨팅 이름을 지정합니다.
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Correct Answer: A  🗳️

Question #3

Azure Storage Blob 컨테이너에 대해 ml-data라는 데이터 저장소를 정의했습니다. 해당 컨테이너에는 train이라는 폴더가 있고, 그 안에 data.csv라는 파일이 있습니다. Azure Machine Learning SDK를 사용하여 이 파일을 모델 학습에 사용할 계획입니다.
Azure Machine Learning SDK를 사용하여 로컬 컴퓨팅 환경에서 실험을 실행함으로써 모델을 학습시킬 계획입니다.
다음 코드를 실행하여 DataReference 객체를 정의합니다.

학습 데이터를 불러와야 합니다.
어떤 코드 부분을 사용해야 할까요?

  • A. 옵션 A
  • B. 옵션 B
  • C. 옵션 C
  • D. 옵션 D
  • E. 옵션 E
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Correct Answer: E  🗳️

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Question #4

Azure Machine Learning 실험으로 Python 스크립트를 실행할 계획입니다.
스크립트는 폴더 계층 구조에서 파일을 읽어야 합니다. 파일은 데이터셋 인수로 스크립트에 전달됩니다.
데이터셋 인수에 적절한 모드를 지정해야 합니다.
어떤 두 가지 모드를 사용할 수 있습니까? 각 정답은 완전한 해결책을 제시합니다.
참고: 정답 하나당 1점입니다.

  • A. to_pandas_dataframe()
  • B. as_upload()
  • C. 마운트()로
  • D. as_download()
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Correct Answer: D  🗳️

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Question #5

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.
IT 부서에서 다음과 같은 Azure 리소스 그룹 및 리소스를 생성합니다.

IT 부서는 Azure Machine Learning 작업 영역에 aks-cluster라는 이름의 Azure Kubernetes Service(AKS) 기반 추론 컴퓨팅 대상을 생성합니다.
GPU가 내장된 Microsoft Surface Book 컴퓨터를 사용하고 있습니다. Python 3.6과 Visual Studio Code가 설치되어 있습니다.
심층 신경망(DNN) 모델을 학습시키고 손실 및 정확도 지표를 기록하는 스크립트를 실행해야 합니다.
해결 방법: mlvm 가상 머신을 Azure Machine Learning 작업 영역의 컴퓨팅 대상으로 연결합니다.
Surface Book에 Azure ML SDK를 설치하고 Python 코드를 실행하여 작업 영역에 연결합니다. mlvm 원격 컴퓨팅 리소스에서 학습 스크립트를 실험으로 실행합니다.

  • A. 예
  • B. 아니요
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Correct Answer: A  🗳️

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