Last Updated: Oct 14, 2026
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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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| Section | Objectives |
|---|---|
| Deploy and consume models | - Monitor deployed models and endpoints - Deploy models to endpoints |
| Explore and analyze data | - Perform exploratory data analysis - Ingest and prepare data for modeling |
| Train machine learning models | - Tune hyperparameters and evaluate models - Train models using Azure Machine Learning |
| Optimize and manage models | - Track experiments and manage model lifecycle - Improve model performance |
| Design and prepare a machine learning solution | - Manage compute and data assets - Select appropriate Azure services for machine learning workloads - Plan and configure Azure Machine Learning workspace |
크라우드소싱 감정 분석 로컬 모델에 대한 특징 엔지니어링 전략을 구현해야 합니다.
어떻게 해야 할까요?
Correct Answer: C 🗳️
Explanation: Only visible for Test4Sure members. You can sign-up / login (it's free).
특징 추출 방법을 선택해야 합니다.
어떤 방법을 사용해야 할까요?
Correct Answer: A 🗳️
Explanation: Only visible for Test4Sure members. You can sign-up / login (it's free).
다음과 같은 Azure 구독 및 Azure Machine Learning 서비스 작업 영역이 있습니다.
mi-protect 워크스페이스에 대한 참조를 얻어야 합니다.
해결 방법: 다음 파이썬 코드를 실행하세요.
이 해결책은 목표를 달성합니까?
Correct Answer: B 🗳️
Azure Machine Learning 데이터 세트를 생성합니다. Azure Machine Learning 디자이너를 사용하여 Python 스크립트 실행 구성 요소와 사용자 지정 코드를 이용해 데이터 세트를 변환합니다.
스크립트와 관련 라이브러리를 스크립트 번들로 업로드해야 합니다.
Python 스크립트 실행 구성 요소를 구성해야 합니다.
어떤 설정을 사용해야 할까요? 답변하려면 답변 영역에서 적절한 옵션을 선택하세요.
참고: 정답 하나당 1점입니다.
Correct Answer:

Explanation:
파이프라인을 실행하는 파이썬 스크립트가 있습니다. 이 스크립트에는 다음 코드가 포함되어 있습니다.
AzureML.core에서 Experiment를 가져옵니다.
pipeline_run = Experiment(ws, 'pipeline_test').submit(pipeline)
스크립트를 배포하기 전에 파이프라인을 테스트하고 싶습니다.
파이프라인 실행이 완료되면 STDOUT 출력에 기록되는 파이프라인 실행 세부 정보를 표시해야 합니다.
테스트 스크립트에 어떤 코드 부분을 추가해야 할까요?
Correct Answer: D 🗳️
Explanation: Only visible for Test4Sure members. You can sign-up / login (it's free).
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