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NEW QUESTION: 1
Click the Exhibit button.

Given the customer Route List index shown in the exhibit, what is the first expensive route that a call placed on that RLI could take?
A. Entry 2
B. Entry 3
C. Entry 1
D. Entry 0
Answer: A

NEW QUESTION: 2
You implement Microsoft Azure Advanced Threat Protection (Azure ATP).
You have an Azure ATP sensor configured as shown in the following exhibit.
Updates

How long after the Azure ATP cloud service is updated will the sensor update?
A. 12 hours
B. 24 hours
C. 7 days
D. 1 hour
E. 48 hours
Answer: B
Explanation:
Explanation
References:
https://docs.microsoft.com/en-us/azure-advanced-threat-protection/atp-whats-new

NEW QUESTION: 3
You have two Hyper-V hosts named Server1 and Server2 that run Windows Server 2016. Server1 and Server2 are connected to the same network.
On Server1 and Server2, you create an external network switch named Switch1.
You have the virtual machines shown in the following table.

All three virtual machines are connected to Switch1.
You need to prevent applications in VM3 from being able to capture network traffic from VM1 or VM2. The solution must ensure that VM1 retains network connectivity.
What should you do?
A. Modify the subnet mask of VM1 and VM2.
B. On Server2, create an external switch and connect VM3 to the switch.
C. On Server2, configure the VLAN ID setting of Switch1.
D. Configure network virtualization for VM1 and VM2.
Answer: D
Explanation:
Explanation/Reference:
Explanation:
References:
https://docs.microsoft.com/en-us/windows-server/networking/sdn/technologies/hyper-v-network- virtualization/hyperv-network-virtualization-technical-details-windows-server
https://blogs.technet.microsoft.com/networking/2016/10/26/network-virtualization-with-ws2016-sdn/

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:

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