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NEW QUESTION: 1
あなたは会社のMicrosoft 365管理者です。
いくつかのユーザーは、PDFが添付された電子メールを受信すると報告しています。 PDF添付ファイルは悪意のあるコードを起動します。
影響を受けるドキュメントが開かれた場合、受信トレイからメッセージを削除し、PDFの脅威を無効にする必要があります。
どの機能を実装する必要がありますか？
A. ゼロアワーの自動パージ
B. メールフロールールを使用したDKIM署名済みメッセージ
C. Microsoft Exchange管理センターのブロックリスト
D. 送信者ポリシーフレームワーク
E. 高度な脅威保護フィッシング対策
Answer: E
Explanation:
References:
https://docs.microsoft.com/en-us/office365/securitycompliance/atp-anti-phishing

NEW QUESTION: 2
Which of the following statements about Flush packets in a Smart Link group are true? (Select 3 Answers)
A. The destination MAC address of Flush packets is an unknown unicast address, which is used to distinguish protocols.
B. The destination MAC address of Flush packets is a special multicast address, which is used to distinguish protocols.
C. A Smart Link group sends Flush packets to instruct other devices to update the address table.
D. Flush packets use IEEE 802.3 encapsulation.
Answer: A,C,D

NEW QUESTION: 3
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

NEW QUESTION: 4
What comprises a failure code?
A. Failure, affect, fix
B. Issue, reason, resolution
C. Situation, impact, solution
D. Problem, cause, remedy
Answer: D
Explanation:
Failure codes are specified on asset records and on location records. Failure codes are linked in parent-child relationships to form a failure hierarchy.

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