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NEW QUESTION: 1
Which DSCP classification is defined in RFC 2474?
A. DSCP AF11
B. DSCP CS2
C. DSCP CS1
D. DSCP 0
E. DSCP AF21
F. DSCP EF
G. DSCP CS6
H. DSCP CS3
Answer: H

NEW QUESTION: 2
Refer to exhibit.

Which two statements about this command are true? (Choose two)
A. The command is usually used for Microsoft network load balancing implementation
B. The command is used to enable unknown unicast flooding for a known MAC address across the overlay
C. The command is used in HSRP localization for OTV
D. The command is used to extend the spanning tree for VLAN 72 across the overlay
E. The command is usually used FCIP over the overlay
Answer: A,B

NEW QUESTION: 3
You enable feedback for a Voice of the Customer survey.
What is the total number of questions allowed for the survey?
A. 0
B. 1
C. 2
D. 3
Answer: B

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