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NEW QUESTION: 1
Azure Machine Learning Studioで実験を作成します。 10,000行を含むトレーニングデータセットを追加します。最初の9,000行はクラス0（90パーセント）を表します。
残りの1,000行はクラス1（10パーセント）を表します。
トレーニングセットは、2つのクラス間の不均衡です。 5つのデータ行を使用して、クラス1のトレーニング例の数を4,000に増やす必要があります。 Synthetic Minority Oversampling Technique（SMOTE）モジュールを実験に追加します。
モジュールを構成する必要があります。
どの値を使用する必要がありますか？回答するには、回答領域のダイアログボックスで適切なオプションを選択します。
注：それぞれの正しい選択には1ポイントの価値があります。

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

NEW QUESTION: 2
Bei der Entwicklung einer risikobasierten Prüfungsstrategie sollte ein IS-Prüfer eine Risikobewertung durchführen, um sicherzustellen, dass:
A. Eine Lückenanalyse ist angebracht.
B. Prüfungsrisiken werden berücksichtigt.
C. Kontrollen zur Risikominderung sind vorhanden.
D. Schwachstellen und Bedrohungen werden identifiziert.
Answer: D
Explanation:
Erläuterung:
Bei der Entwicklung einer risikobasierten Prüfungsstrategie ist es entscheidend, dass die Risiken und Schwachstellen verstanden werden. Dies bestimmt die zu prüfenden Bereiche und den Umfang der Berichterstattung. Das Verständnis, ob geeignete Kontrollen zur Risikominderung erforderlich sind, ist eine Folge der Prüfung. Prüfungsrisiken sind inhärente Aspekte der Prüfung, stehen in direktem Zusammenhang mit dem Prüfungsprozess und sind für die Risikoanalyse des zu prüfenden Umfelds nicht relevant. Eine Lückenanalyse würde normalerweise dazu dienen, den tatsächlichen Zustand mit einem erwarteten oder erwünschten Zustand zu vergleichen.

NEW QUESTION: 3
Azure 구독이 있습니다.
Azure Resource Manager 템플릿을 사용하여 동일한 가용성 집합의 일부가 될 50 개의 Azure 가상 컴퓨터를 배포하려고 합니다.
패브릭이 고장 나거나 서비스 중일 때 가능한 많은 가상 머신을 사용할수 있도록 해야 합니다.
템플릿을 어떻게 구성해야 합니까? 답변하려면 답변 영역에서 적절한 옵션을 선택하십시오.
참고 : 각각의 올바른 선택은 1 포인트의 가치가 있습니다.

아래 두 가지 대안을 선택하십시오.
A. platformFaultDomainCount : 0
B. platformUpdateDomainCount : 30
C. platformUpdateDomainCount : 20
D. platformUpdateDomainCount : 50
E. platformFaultDomainCount : 4
F. platformFaultDomainCount : 3
G. platformUpdateDomainCount : 25
H. platformFaultDomainCount : 1
I. platformUpdateDomainCount : 10
J. platformUpdateDomainCount : 40
K. platformFaultDomainCount : 2
Answer: C,K
Explanation:
설명
두 개의 장애 도메인을 사용하십시오.
현재 거주하는 지역에 따라 2 또는 3이 최대입니다.
platformUpdateDomainCount에 20을 사용하십시오.
업데이트 도메인 (platformUpdateDomainCount)을 늘리면 플랫폼이 노드를 재부팅 할 때 용량 및 가용성 계획에 도움이 됩니다. 풀의 숫자가 높을수록 (최대 20 개) 주어진 가용성 집합에서 노드가 적은 수의 노드가 한 번에 재부팅됨을 의미합니다.
참고 문헌 :
https://www.itprotoday.com/microsoft-azure/check-if-azure-region-supports-2-or-3-fault-domains-managed-disk
https://github.com/Azure/acs-engine/issues/1030