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NEW QUESTION: 1
Which of the following is an advantage of using bindings in your Azure Functions to access data sources and data sinks?
A. They provide access to more data sources than are available using code.
B. They let you connect to Azure resources without authentication.
C. They simplify the connection process; you don't need to code specific connection logic.
Answer: C

NEW QUESTION: 2
A government-initiative project has a strict deadline for completion. A kick-off meeting is a requirement, but one critical stakeholder is unavailable to attend Postponing this meeting will delay the project What should the project manager do?
A. Document that the stakeholder is unavailable and proceed with the kick-off meeting.
B. Delay the project and accept the governmental penalties.
C. Document the issue, proceed with the kick-off meeting, and speak with the unavailable stakeholder at a later time
D. Postpone the project until the stakeholder is available, but plan for the team to work overtime to meet the deadline.
Answer: C

NEW QUESTION: 3
Sie stellen eine Exchange Server 2013-Organisation zur Evaluierung in einem Testnetzwerk bereit.
Sie installieren die Postfachserverrolle und die Clientzugriffsserverrolle auf einem Server mit dem Namen ex01.contoso.com. Sie führen keine anderen Konfigurationen durch.
Alle Ports vom Internet zu ex01.contoso.com sind geöffnet. Sie stellen erfolgreich eine Verbindung zu ex01.contoso.com aus dem Internet und dem internen Netzwerk her.
Sie müssen ermitteln, welche Arten von Exchange Server-Clients ohne weitere Konfigurationen eine erfolgreiche Verbindung zur Organisation herstellen.
Was solltest du identifizieren?
A. Interne IMAP-Clients
B. Interne Microsoft Outlook-Verbindungen
C. Windows RT-Geräte, auf denen die Mail-App ausgeführt wird
D. Android-Telefone mit Activesync
Answer: B

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