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NEW QUESTION: 1
다음 신뢰 서비스 원칙 중 다음 중 타사 제공 업체의 고객에게 제공되는 시스템, 제품 또는 서비스에서 사용하는 정보의 액세스 가능성을 나타내는 것은 무엇입니까?
A. 개인 정보
B. 액세스
C. 보안
D. 가용성
Answer: D
Explanation:
Availability refers to the accessibility of information used by the entity's systems as well as the products or services provided to its customers. The availability objective does not, in itself, set a minimum acceptable performance level; it does not address system functionality (the specific functions a system performs) or usability (the ability of users to apply system functions to the performance of specific tasks or problems). However, it does address whether systems include controls to support accessibility for operation, monitoring, and maintenance.

NEW QUESTION: 2
Hinweis: Diese Frage ist Teil einer Reihe von Fragen, die dasselbe Szenario darstellen. Jede Frage in der Reihe enthält eine eindeutige Lösung, mit der die angegebenen Ziele erreicht werden können. Einige Fragensätze haben möglicherweise mehr als eine richtige Lösung, während andere möglicherweise keine richtige Lösung haben.
Nachdem Sie eine Frage in diesem Abschnitt beantwortet haben, können Sie NICHT mehr darauf zurückkommen. Infolgedessen werden diese Fragen nicht im Überprüfungsbildschirm angezeigt.
Sie sind der Systemadministrator einer Vertriebsgesellschaft. Sie verwalten die Microsoft Office 365-Umgebung für Ihr Unternehmen. Sie untersuchen die Anforderungen für die Verhinderung von Datenverlust in Office 365 (DLP). Alle Benutzer haben eine Enterprise E5-Lizenz.
Sie müssen sicherstellen, dass alle Benutzer keine Sozialversicherungsnummern in Office 365 speichern können.
Sie müssen eine DLP-Richtlinie implementieren, die die Anforderungen erfüllt.
Lösung: Erstellen Sie eine DLP-Richtlinie mit Microsoft Exchange Online als Speicherort für Inhaltsquellen.
Entspricht die Lösung dem Ziel?
A. Ja
B. Nein
Answer: B

NEW QUESTION: 3
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

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• Microsoft Microsoft Certified: Cybersecurity Architect Expert
• Microsoft Certified Entry Networking Technician