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NEW QUESTION: 1
Scenario: A Citrix Architect is designing a new XenApp and XenDesktop environment and needs to determine which endpoints to include. Based on discussions with various departments, the user requirements listed in the Exhibit were collected.
Click the Exhibit button to view the requirements.

In addition to the user requirements, the architect has identified the following organizational requirements:
* Minimize total cost of ownership (TCO)
* Minimize maintenance costs
Which endpoint type should the architect use for the Product Development group, based on the requirements?
A. Corporate-owned laptop/notebook
B. BYOD laptop/notebook
C. Corporate-owned standard desktop
D. Corporate-owned thin client
E. Corporate-owned high-performance workstation
Answer: E

NEW QUESTION: 2
非排他的デバッグモードとはどういう意味ですか？該当するものをすべて選択。
A. コミットにより、データベースでデータセットに不整合が発生する可能性があります。
B. データベースカーソルはCOMMITを使用して閉じる必要があるため、ステートメントSELECTとENDSELECT '-'の間ではデバッグできません。
C. 各デバッガービューの後に、ロールアウトがアプリケーションで強制されます。
D. 変換またはフィールド出口のデバッグはできません
E. 誰かが同じソースコードをデバッグしています
F. 風景のどこにでも使用できます。
Answer: A,B,C,D

NEW QUESTION: 3
What does the protocol ARP do?
A. Facilitates file transfers
B. Takes an IP address and finds out the MAC address to which it
belongs
C. Takes a MAC address and finds an IP address to match
D. Sends messages to the devices regarding the health of the network
Answer: B
Explanation:
The correct answer is "Takes an IP address and finds out the MAC address to which it belongs". ARP starts with an IP address, then queries the network to find the MAC or hardware address of the workstation to which it belongs.
ICMP performs "Sends messages to the devices regarding the health of the network".
RARP performs "Takes a MAC address and finds an IP address to match".
FTP performs "Facilitates file transfers".

NEW QUESTION: 4
You are evaluating a Python NumPy array that contains six data points defined as follows:
data = [10, 20, 30, 40, 50, 60]
You must generate the following output by using the k-fold algorithm implantation in the Python Scikit-learn machine learning library:
train: [10 40 50 60], test: [20 30]
train: [20 30 40 60], test: [10 50]
train: [10 20 30 50], test: [40 60]
You need to implement a cross-validation to generate the output.
How should you complete the code segment? To answer, select the appropriate code segment in the dialog box in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation:
Box 1: k-fold
Box 2: 3
K-Folds cross-validator provides train/test indices to split data in train/test sets. Split dataset into k consecutive folds (without shuffling by default).
The parameter n_splits ( int, default=3) is the number of folds. Must be at least 2.
Box 3: data
Example: Example:
>>>
>>> from sklearn.model_selection import KFold
>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
>>> y = np.array([1, 2, 3, 4])
>>> kf = KFold(n_splits=2)
>>> kf.get_n_splits(X)
2
>>> print(kf)
KFold(n_splits=2, random_state=None, shuffle=False)
>>> for train_index, test_index in kf.split(X):
... print("TRAIN:", train_index, "TEST:", test_index)
... X_train, X_test = X[train_index], X[test_index]
... y_train, y_test = y[train_index], y[test_index]
TRAIN: [2 3] TEST: [0 1]
TRAIN: [0 1] TEST: [2 3]
References:
https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html