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NEW QUESTION: 1
A. Option C
B. Option D
C. Option B
D. Option A
Answer: A,D
NEW QUESTION: 2
新しいAzure Active Directory（Azure AD）ロールを作成する予定です。
新しいロールがAzureサブスクリプションのすべてのリソースを表示し、Microsoftにサポートリクエストを発行できることを確認する必要があります。ソリューションは、最小特権の原則を使用する必要があります。
JSON定義をどのように完成させる必要がありますか？回答するには、回答エリアで適切なオプションを選択します。
注：各正しい選択には1ポイントの価値があります。
Answer:
Explanation:
Explanation
Box 1: "*/read",
*/read lets you view everything, but not make any changes.
Box 2: " Microsoft.Support/*"
The action Microsoft.Support/* enables creating and management of support tickets.
References:
https://docs.microsoft.com/en-us/azure/role-based-access-control/tutorial-custom-role-powershell
https://docs.microsoft.com/en-us/azure/role-based-access-control/built-in-roles
NEW QUESTION: 3
A. Option D
B. Option B
C. Option C
D. Option A
Answer: D
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
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