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NEW QUESTION: 1
When splitting message flow processing between different integration servers how do the flows communicate?
A. Using switch addresses and node configuration
B. Using a connectivity server and switch agents
C. Using node addresses and node configuration
D. Using a switch server and connectivity agents
Answer: D
Explanation:
Explanation
If you are splitting processing between different integration servers, or between IBM App Connect Enterprise and IBM App Connect on IBM Cloud, your flows communicate by using a Switch server and connectivity agents.
https://www.ibm.com/support/knowledgecenter/SSTTDS_11.0.0/com.ibm.etools.mft.doc/cl23141_.htm

NEW QUESTION: 2
What is one benefit of creating dashboard panels from reports?
A. Any change to the underlying report will affect every dashboard that utilizes that report.
B. There are no benefits to creating dashboard panels from reports.
C. Any newly created dashboard will include that report.
D. It makes the dashboard more efficient because it only has to run one search string.
Answer: D

NEW QUESTION: 3
What does a 'risk management strategy' describe?
A. Risk management policies that will be implemented throughout an organization
B. Processes required to improve risk management within an organization
C. Series of steps needed to implement risk management
D. Specific risk management practices that will be performed within an activity
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