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NEW QUESTION: 1
MyCo, a mobile company, uses Pega Marketing, powered by Pega Customer Decision Hub, to display
marketing offers to its customers on its website. The company would like to display more relevant offers to
customers based on customer behavior. The following is the proposition hierarchy in the Next-Best-Action
Designer.
Both the groups are configured with a relevancy strategy and eligibility criteria.
The company would like to arbitrate across the offers in the two groups and select the best offer based on
customer behavior.
As a Strategy Designer, what must you configure in order to meet this requirement?
A. Propensity at the group level
B. Target audience
C. Prioritization strategy
D. Metrics of the adaptive model
Answer: B
NEW QUESTION: 2
新しいDynamics365Financeインスタンスを作成します。
複数のサードパーティの金融システムからDynamics365Financeインスタンスにデータを移行する必要があります。関連するすべてのデータを収集します。
サードパーティのデータをDynamics365Financeエンティティにマップする必要があります。
どのエンティティカテゴリを使用する必要がありますか？回答するには、適切なエンティティカテゴリを正しいソースデータにドラッグします。各エンティティカテゴリは、1回使用することも、複数回使用することも、まったく使用しないこともできます。コンテンツを表示するには、分割バーをペイン間でドラッグするか、スクロールする必要がある場合があります。
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:
Explanation
Reference:
https://docs.microsoft.com/en-us/dynamics365/fin-ops-core/dev-itpro/data-entities/data-entities
NEW QUESTION: 3
Um die Sicherheit in SDLC-Prozesse (System Development Fie Cycle) zu integrieren, MUSS eine Organisation diese Sicherheit gewährleisten.
A. Ist eine Voraussetzung für den Abschluss der Hauptphasen
B. Leistungsmetriken wurden erfüllt.
C. Rollen und verantwortungsbewusst wurden definiert
D. Wird auf der Konfigurationssteuerkarte dargestellt.
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