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Exam Code: AWS-DevOps

Exam Name: AWS Certified DevOps Engineer - Professional (DOP-C01)

Certification Provider: Amazon

Related Certification: AWS Certified DevOps Engineer - Professional (DOP-C01)

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NEW QUESTION: 1
참고 :이 질문은 동일한 시나리오를 나타내는 일련의 질문 중 일부입니다. 시리즈의 각 질문에는 명시된 목표를 달성할수 있는 고유한 솔루션이 포함되어 있습니다. 일부 질문 세트에는 둘 이상의 올바른 솔루션이 있을 수 있지만 다른 질문 세트에는 올바른 솔루션이 없을 수 있습니다.
이 섹션의 질문에 대답한 후에는 해당 질문으로 돌아올 수 없습니다. 결과적으로 이러한 질문은 검토 화면에 나타나지 않습니다.
VM1이라는 Azure 가상 컴퓨터가 있습니다. VM1은 ARM1.json이라는 사용자 지정 Azure Resource Manager 템플릿을 사용하여 배포되었습니다.
VM1이 유지 관리의 영향을 받는다는 알림을 받습니다.
VM1을 다른 호스트로 즉시 이동해야 합니다.
솔루션 : 재배치 블레이드에서 재배포를 클릭합니다.
이것이 목표를 달성합니까?
A.
B. 아니요
Answer: A
Explanation:
설명
VM을 다시 배포하면 VM이 Azure 인프라 내의 새 노드로 VM을 이동한 다음 전원을 다시 켜서 모든 구성 옵션 및 관련 리소스를 유지합니다.
참조 : https://docs.microsoft.com/en-us/azure/virtual-machines/windows/redeploy-to-new-node

NEW QUESTION: 2
You create an experiment in Azure Machine Learning Studio. You add a training dataset that contains 10,000 rows. The first 9,000 rows represent class 0 (90 percent).
The remaining 1,000 rows represent class 1 (10 percent).
The training set is imbalances between two classes. You must increase the number of training examples for class 1 to 4,000 by using 5 data rows. You add the Synthetic Minority Oversampling Technique (SMOTE) module to the experiment.
You need to configure the module.
Which values should you use? To answer, select the appropriate options in the dialog box in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation

Box 1: 300
You type 300 (%), the module triples the percentage of minority cases (3000) compared to the original dataset (1000).
Box 2: 5
We should use 5 data rows.
Use the Number of nearest neighbors option to determine the size of the feature space that the SMOTE algorithm uses when in building new cases. A nearest neighbor is a row of data (a case) that is very similar to some target case. The distance between any two cases is measured by combining the weighted vectors of all features.
By increasing the number of nearest neighbors, you get features from more cases.
By keeping the number of nearest neighbors low, you use features that are more like those in the original sample.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote


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