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NEW QUESTION: 1
In Microsoft 365, you configure a data loss prevention (DLP) policy named Policy1. Policy1 detects the sharing of United States (US) bank account numbers in email messages and attachments.
Policy1 is configured as shown in the exhibit. (Click the Exhibit tab.)

You need to ensure that internal users can email documents that contain US bank account numbers to external users who have an email suffix of contoso.com.
What should you configure?
A. a group
B. an action
C. an exception
D. a condition
Answer: B
Explanation:
Explanation
References:
https://docs.microsoft.com/en-us/office365/securitycompliance/data-loss-prevention-policies#how-dlp-policies-w

NEW QUESTION: 2
どのステートメントが名前付きEIGRP構成を説明していますか？
A. 名前付きEIGRP構成は階層的に構成されています
B. 名前付きEIGRP構成は、構成ミスの可能性を高める可能性があります
C. 名前付きEIGRP構成はアドレスファミリの概念を使用します
D. 名前付きEIGRP構成はIPv4およびIPv6構成を統合します
E. 名前付きEIGRP構成は、多くの構成モードで実行されます
Answer: A,C,D

NEW QUESTION: 3
二項分類モデルを作成して、人が病気にかかっているかどうかを予測します。
考えられる分類エラーを検出する必要があります。
説明ごとにどのエラータイプを選択する必要がありますか？回答するには、回答領域で適切なオプションを選択します。
注：正しい選択はそれぞれ1ポイントの価値があります。

Answer:
Explanation:

Explanation:
Box 1: True Positive
A true positive is an outcome where the model correctly predicts the positive class Box 2: True Negative A true negative is an outcome where the model correctly predicts the negative class.
Box 3: False Positive
A false positive is an outcome where the model incorrectly predicts the positive class.
Box 4: False Negative
A false negative is an outcome where the model incorrectly predicts the negative class.
Note: Let's make the following definitions:
"Wolf" is a positive class.
"No wolf" is a negative class.
We can summarize our "wolf-prediction" model using a 2x2 confusion matrix that depicts all four possible outcomes:
Reference:
https://developers.google.com/machine-learning/crash-course/classification/true-false-positive-negative