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: Nov 14, 2026
- Q & A: 528 Questions and Answers
About Microsoft DP-100 Korean Exam
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Obligatory Prerequisites
Officially, there is no prior work-experience or educational expertise related to DP-100 exam. Anyone, willing to make it big in the world of data science, can go for it. However, industry pundits say that beginner-level expertise in concepts like running data experiments and machine learning will make the exam journey a lot more simplified and easy to accomplish.
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Career Opportunities, Roles, and Salary
After obtaining the Microsoft Certified: Azure Data Scientist Associate certification, you become eligible for the data scientist role. Also, with this certification, you can expect to earn a decent remuneration of about $97k on average annually, as stated by PayScale.com. The expected career paths for a data scientist also include senior data scientist with a salary potential of $126,778 and data science manager with a salary potential of $136,781. Another possible path is that of a data science director with a potential pay of $157,095 annually. However, remember that besides, such a certificate work experience also matters.
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
Microsoft DP-100 Korean Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Explore data and train models | 35-40% | - Optimize model performance
|
| Topic 2: Deploy and retrain models | 10-15% | - Implement retraining pipelines
|
| Topic 3: Design and prepare a machine learning solution | 20-25% | - Design an Azure Machine Learning workspace
|
| Topic 4: Prepare a model for deployment | 20-25% | - Deploy machine learning models
|
Microsoft Designing and Implementing a Data Science Solution on Azure (DP-100 Korean Version) Sample Questions:
Workspace 1 Workspace!라는 이름의 Azure Machine Learning 작업 영역이 있으며, 이 작업 영역에는 PyFunc 플레이버를 사용하는 model 1이라는 이름의 MLflow 모델이 등록되어 있습니다. Azure Machine Learning Python SDK vl을 사용하여 이그레스 연결 없이 endpoint1이라는 이름의 온라인 엔드포인트에 model1을 배포할 계획입니다. 다음 코드가 있습니다.
모델이 성공적으로 배포되도록 하려면 ManagedOnlineDeployment 객체에 매개변수를 추가해야 합니다. 해결 방법: scoring_script 매개변수를 추가하세요.
이 해결책은 목표를 달성합니까?
- A. 아니요
- B. 예
Correct Answer: B 🗳️
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.
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
Azure Machine Learning Designer를 사용하여 다음 데이터 세트를 실험에 로드합니다.
데이터 세트 1
데이터셋 2
입력 데이터셋과 동일한 열과 헤더 행을 가지면서 두 입력 데이터셋의 모든 행을 포함하는 데이터셋을 생성해야 합니다.
해결 방법: [변환 적용] 구성요소를 사용하십시오.
이 해결책은 목표를 달성합니까?
- A. 아니요
- B. 예
Correct Answer: A 🗳️
다음과 같은 수치형 특징(X, Y, Z)을 포함하는 특징 집합이 있습니다.
X, Y, Z 특징 간의 포아송 상관 계수(r 값)는 다음 이미지에 나타나 있습니다.
아래 드롭다운 메뉴를 이용하여 그림에 제시된 정보를 바탕으로 각 질문에 대한 적절한 답을 선택하세요.
참고: 정답 하나당 1점입니다.
Correct Answer:
Explanation:
Box 1: 0.859122
Box 2: a positively linear relationship
+1 indicates a strong positive linear relationship
-1 indicates a strong negative linear correlation
0 denotes no linear relationship between the two variables.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/compute-linear-correlation
당신은 호텔 예약 웹사이트 회사에서 일하는 데이터 과학자입니다. Azure Machine Learning 서비스를 사용하여 사기 거래를 식별하는 모델을 학습시키고 있습니다.
Azure Machine Learning SDK의 Model.deploy 메서드를 사용하여 모델을 Azure Machine Learning 실시간 웹 서비스로 배포해야 합니다. 배포된 웹 서비스는 입력된 거래 데이터를 기반으로 사기 여부를 실시간으로 예측해야 합니다.
모델을 배포하는 데 사용되는 InferenceConfig 클래스의 entry_script 매개변수로 지정된 스크립트를 생성해야 합니다.
진입 스크립트는 어떤 작업을 수행해야 할까요?
- A. 적절한 태그와 속성을 사용하여 모델을 등록합니다.
- B. 웹 서비스가 배포된 추론 클러스터에서 노드를 시작합니다.
- C. 추론 연산에 필요한 코어 수와 메모리 용량을 지정합니다.
- D. 모델을 로드하고 이를 사용하여 입력 데이터로부터 레이블을 예측합니다.
- E. 웹 서비스 컴퓨팅을 위한 Conda 환경을 생성하고 필요한 Python 패키지를 설치합니다.
Correct Answer: D 🗳️
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