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NEW QUESTION: 1
Which of the following best defines a Computer Security Incident Response Team (CSIRT)?
A. An organization that disseminates incident-related information to its constituency and other involved parties.
B. An organization that provides a secure channel for receiving reports about suspected security incidents.
C. An organization that coordinates and supports the response to security incidents.
D. An organization that ensures that security incidents are reported to the authorities.
Answer: C
Explanation:
Explanation/Reference:
RFC 2828 (Internet Security Glossary) defines a Computer Security Incident Response Team (CSIRT) as an organization that coordinates and supports the response to security incidents that involves sites within a defined constituency. This is the proper definition for the CSIRT. To be considered a CSIRT, an organization must provide a secure channel for receiving reports about suspected security incidents, provide assistance to members of its constituency in handling the incidents and disseminate incident- related information to its constituency and other involved parties. Security-related incidents do not necessarily have to be reported to the authorities.
Source: SHIREY, Robert W., RFC2828: Internet Security Glossary, may 2000.
NEW QUESTION: 2
A. Option B
B. Option A
C. Option C
D. Option D
Answer: A
NEW QUESTION: 3
You were considering proposing an HPE MSA to a customer who requires a primary storage solution for the virtualized data center. However further discussion revealed that the customer needs more advanced data services. What is a good alternative solution to meet these needs?
A. HPE StoreEasy
B. HPE D-Series
C. HPE StoreOnce VSA
D. HPE SimpliVity
Answer: D
NEW QUESTION: 4
分類を使用してイベントを予測するモデルを開発しています。
次の展示に示すように、テストデータでスコアリングされたモデルの混同行列があります。
ドロップダウンメニューを使用して、図に示されている情報に基づいて各ステートメントを完了する回答の選択肢を選択します。
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: 11
TP = True Positive.
The class labels in the training set can take on only two possible values, which we usually refer to as positive or negative. The positive and negative instances that a classifier predicts correctly are called true positives (TP) and true negatives (TN), respectively. Similarly, the incorrectly classified instances are called false positives (FP) and false negatives (FN).
Box 2: 1,033
FN = False Negative
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance