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NEW QUESTION: 1
Which two are Fibre Channel topologies? (Choose two.)
A. port-to-multipoint
B. spine and leaf topology
C. port-to-port
D. star topology
E. arbirated loop
Answer: C,E

NEW QUESTION: 2
A company has a warehouse that requires ruggedized APs to provide wireless access The APs need to support these characteristics:
* 2 or more spatial streams
* 802.11 ac and 802.11 n support
Cat 7 Ethernet cabling will be used to connect the APs to POE+ capable switches Link aggregation should be employed between the APs and the Ethernet switches. Which would be the most cost-effective solution that would meet the company's requirements?
A. AP 375 with external omnidirectional antennas
B. AP 510 with external omnidirectional antennas and plastic AP cover
C. AP 318 with external omnidirectional antennas
D. AP 365 with integrated omnidirectional antennas
Answer: C

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