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NEW QUESTION: 1
IAM 사용자에게는 기본적으로 페더레이션 사용자 및 역할에 대한 임시 보안 자격 증명을 생성 할 권한이 없습니다. 반대로, IAM 사용자는 특별한 권한없이 __________에 전화 할 수 있습니다
A. GetFederationName
B. GetSessionName
C. GetFederationToken
D. GetSessionToken
Answer: D
Explanation:
설명
현재 STS API 명령 GetSessionToken은 사전 허가없이 계정의 모든 IAM 사용자가 사용할 수 있습니다. 반대로 GetFederationToken 명령은 제한되어 있으며 사용자가이 특정 작업에 대한 호출을 실행할 수 있도록 명시적인 권한을 부여해야 합니다.
http://docs.aws.amazon.com/STS/latest/UsingSTS/STSPermission.html

NEW QUESTION: 2
チケットのダッシュボードで問題を解決する必要があります。
どの構成を変更する必要がありますか？回答するには、回答領域で適切なオプションを選択します。
注：正しい選択はそれぞれ1ポイントの価値があります。

Answer:
Explanation:

NEW QUESTION: 3
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