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NEW QUESTION: 1
To operate DATA ONTAP 8.1 Cluster Mode SAN, what must be configured on both the initiator and the target?
A. Asymmetric Logical Unit Access
B. Symmetric Logical Unit Access
C. Physical Unit Access Initialization
D. Logical Unit Access Initialization
E. SAN Access Initialization Protocol
Answer: A
Explanation:
https%3A%2F%2Fcommunities.netapp.com%2Fservlet%2FJiveServlet%2FpreviewBody%2F5463 -102-1-7794%2FPS%2520Tech%2520Note%2520055%2520-% 2520ALUA.pdf&ei=4Wh2U4vdGNCTqAa10YLIAw&usg=AFQjCNFWKjWIbgDP3ORIHeHptaU0t8y Eow&sig2=Z F-ON4h0kIlmRMTZdP2h3g

NEW QUESTION: 2
Cisco ASAのオンボックスグラフィカルな管理ソリューションは、どのオプションですか？
A. CSM
B. Console
C. SSH
D. ASDM
Answer: D

NEW QUESTION: 3
別紙をご参照ください。スイッチはトランクリンクでどのアクションを実行しますか？

A. トランクは形成されませんが、VLAN 99およびVLAN 999はリンクを通過できます。
B. トランクは形成されますが、VLAN 99およびVLAN 999はシャットダウン状態です。
C. トランクは形成されますが、一致しないネイティブVLANは単一のブロードキャストドメインにマージされます。
D. トランクは形成されず、ポートはerr-disabledステータスになります。
Answer: C
Explanation:
The trunk still forms with mismatched native VLANs and the traffic can actually flow between mismatched switches. But it is absolutely necessary that the native VLANs on both ends of a trunk link match; otherwise a native VLAN mismatch occurs, causing the two VLANs to effectively merge. For example with the above configuration, SW1 would send untagged frames for VLAN 999. SW2 receives them but would think they are for VLAN 99 so we can say these two VLANs are merged.

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