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NEW QUESTION: 1
Refer to the exhibit. How should the FastEthernet0/1 ports on the 2950 model switches that are shown in the exhibit be configured to allow connectivity between all devices?

A: The ports only need to be connected by a crossover cable.
B:

C:

D:

A. Option D
B. Option C
C. Option B
D. Option A
Answer: C
Explanation:
Explanation/Reference:
Explanation:
IN order for multiple VLANs to cross switches, the connection between the switches must be a trunk. The
"switchport mode trunk" command is all that is needed, the individual VLANs should not be listed over that trunk interface.

NEW QUESTION: 2
Note: This question is part of a series of questions that use the same similar answer choices An answer choice may be correct for more than one question in the series Each question is independent of the other questions in this series. Information and details provided in a question apply only to that question
You have a Hyper-V host named Server1 that runs Windows Server 2016 and a two-node scale-out file server cluster named Cluster1
A virtual machine named VM1 runs on Server1
You need to migrate the storage on VM1 to Cluster1
Which tool should you use?
A. the Server Manager Desktop app
B. the Computer Management console
C. the configurehyperv.exe command
D. the Disk Management console
E. the Hyper-V Manager console
F. the Failover Cluster Manager console
G. the cluster.exe command
H. the clussvc.exe command
Answer: E

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