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NEW QUESTION: 1
Refer to Exhibit:

A network administrator is configuring an MPLS and VPLS Martini to connect customer Sites 1, 2, and 3. The administrator has created Virtual Switch Instance (VSI) "CustomerA" for this purpose. How should the administrator connect customer Site 1 to the VPLS solution?
A. Create a service instance on the VLAN assigned to g1/0/1. Also create a global cross-connect group, and bind the CustomerA VSI and the service instance in that group.
B. Create a service instance on g1/0/1. Bind (cross-connect) that instance to the CustomerA VSI.
C. Create a service instance on g1/0/1. Also create a global cross-connect group, and bind the CustomerA VSI and the service instance in that group.
D. Create a service instance on the VLAN assigned to g1/0/1. Bind (cross-connect) that instance to the CustomerA VSI.
Answer: C

NEW QUESTION: 2
Information that could lead to the compromise of vital national defense plans or complex cryptologic and communications intelligence systems should be classified:
A. Secret
B. Cosmic
C. Restricted
D. Top secret
E. Confidential
Answer: D

NEW QUESTION: 3
You have a dataset created for multiclass classification tasks that contains a normalized numerical feature set with 10,000 data points and 150 features.
You use 75 percent of the data points for training and 25 percent for testing. You are using the scikit-learn machine learning library in Python. You use X to denote the feature set and Y to denote class labels.
You create the following Python data frames:

You need to apply the Principal Component Analysis (PCA) method to reduce the dimensionality of the feature set to 10 features in both training and testing sets.
How should you complete the code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation:
Box 1: PCA(n_components = 10)
Need to reduce the dimensionality of the feature set to 10 features in both training and testing sets.
Example:
from sklearn.decomposition import PCA
pca = PCA(n_components=2) ;2 dimensions
principalComponents = pca.fit_transform(x)
Box 2: pca
fit_transform(X[, y])fits the model with X and apply the dimensionality reduction on X.
Box 3: transform(x_test)
transform(X) applies dimensionality reduction to X.
References:
https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html