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NEW QUESTION: 1
Which of the following about the default MDT in Draft Rosen is TRUE?
A. It is also known as the S-PMSI. Each MVPN may have multiple S-PMSIs.
B. It is also known as the I-PMSI. Each MVPN has only one I-PMSI.
C. It is also known as the I-PMSI. Each MVPN may have multiple I-PMSIs.
D. It is also known as the S-PMSI. Each MVPN has only one S-PMSI.
Answer: B
NEW QUESTION: 2
An administrator needs to deploy a large number of handhelds in a manner that meets the following criteria: The solution must be cost effective and require a low amount of administrative overhead. Which solution would accomplish this? (Choose one)
A. Set the "Skip Enterprise Activation Password Requirement" option to True in the BlackBerry Administration Console
B. Add the user to the BlackBerry Enterprise Server and set an Activation Password, then have the users activate
C. Activate the BlackBerry devices using the BlackBerry Administration Console
D. Deploy BlackBerry Web Desktop Manager to allow BlackBerry device users to set an Enterprise Activation password and activate
E. Deploy BlackBerry Desktop Manager and have users activate with BlackBerry Desktop Manager
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
NEW QUESTION: 4
You must route both IPv4 and IPv6 packets in your environment. Which configuration should you add at the [edit protocols] level to allow you to accomplish this objective?
A. ospf {
area 0.0.0.0 {
interface all;
family inet6;
}
}
B. ospf3 {
area 0.0.0.0 {
interface all;
allow ipv4;
}
}
C. ospf {
area 0.0.0.0 {
interface all;
}
}
D. ospf3 {
area 0.0.0.0 {
interface all;
}
}
Answer: D