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NEW QUESTION: 1
展示に示されているように、VNet1という名前の仮想ネットワークがあります。 （展示をクリックします。）

VNet1に接続されているデバイスはありません。
同じ地域にあるVNet2という名前の別の仮想ネットワークにVNet1をピアリングする予定です。 VNet2のアドレス空間は10.2.0.0/16です。
ピアリングを作成する必要があります。
最初に何をすべきですか？
A. VNet1のアドレス空間を変更します。
B. VNet2でサービスエンドポイントを構成します。
C. ゲートウェイサブネットをVNet1に追加します。
D. VNet1およびVNet2にサブネットを作成します。
Answer: A
Explanation:
Explanation
The virtual networks you peer must have non-overlapping IP address spaces. The exhibit indicates that VNet1 has an address space of 10.2.0.0/16, which is the same as VNet2, and thus overlaps. We need to change the address space for VNet1.
References:
https://docs.microsoft.com/en-us/azure/virtual-network/virtual-network-manage-peering#requirements-and-const

NEW QUESTION: 2
Pete, an IT Administrator, needs to secure his server room. Which of the following mitigation methods would provide the MOST physical protection?
A. HVAC
B. Sign in and sign out logs
C. Video surveillance
D. Mantrap
Answer: D
Explanation:
Mantraps are designed to contain an unauthorized, potentially hostile person/individual physically until authorities arrive. Mantraps are typically manufactured with bulletproof glass, high-strength doors, and locks and to allow the minimal amount of individuals depending on its size. Some mantraps even include scales that will weigh the person. The doors are designed in such a way as to open only when the mantrap is occupied or empty and not in-between. This means that the backdoor must first close before the front door will open. Mantraps are in most cases also combined with guards. This is the most physical protection any one measure will provide.

NEW QUESTION: 3
Windows 10デバイスのコンプライアンス要件を満たす必要があります。
Intune管理センターから何を作成する必要がありますか？
A. アプリ構成ポリシー
B. デバイス構成プロファイル
C. アプリケーションポリシー
D. デバイスコンプライアンスポリシー
Answer: A

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