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NEW QUESTION: 1
大規模なモノリシックアプリケーションを再構築する際に推奨されるクラウドアーキテクチャの設計原則はどれですか。 （2つ選択してください。）
A. 固定サーバーを使用します。
B. 個々のコンポーネントに依存します。
C. スケーラビリティの設計。
D. 手動監視を使用します。
E. 疎結合を実装します。
Answer: C,E
Explanation:
Explanation
https://www.architech.ca/re-architect-applications/

NEW QUESTION: 2
-- Exhibit -

-- Exhibit -
Please refer to the attached incident response procedure. Which of the following would Joe, an administrator, do NEXT after discovering one of his critical servers has been breached?
A. Hash an image of the machine and isolate it from the network
B. Immediately start to restore backups for the systems
C. Notify his supervisor or next available manager in the chain of command
D. Take a copy of the machines, including memory
Answer: C

NEW QUESTION: 3
Which two statements are true for mailbox disks per node in an HA pair? (Choose two.)
A. Four disks are required if the root volume is on a mirrored aggregate.
B. Six disks are required if the root volume is on a mirrored aggregate.
C. Eight disks are required if the root volume is on partitioned disks.
D. One array LUN is required if the root volume is on a storage array.
E. Two disks are required if the root volume is on a disk shelf.
Answer: D,E

NEW QUESTION: 4
次のように定義された6つのデータポイントを含むPython NumPy配列を評価しています。
データ= [10、20、30、40、50、60]
Python Scikit-learn機械学習ライブラリのk-foldアルゴリズムの埋め込みを使用して、次の出力を生成する必要があります。
train: [10 40 50 60], test: [20 30]
train: [20 30 40 60], test: [10 50]
train: [10 20 30 50], test: [40 60]
出力を生成するには、相互検証を実装する必要があります。
どのようにコードセグメントを完成させるべきですか？回答するには、回答領域のダイアログボックスで適切なコードセグメントを選択します。
注：それぞれの正しい選択には1ポイントの価値があります。

Answer:
Explanation:

Explanation

Box 1: 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