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NEW QUESTION: 1
A customer who has a POWER6 server would like to buy a POWER7+ server and wants to use one 1 2X PCI-X drawer.
Which POWER7+ models will meet customer requirement?
A. 4-core Power 720 8-core Power 740 8-core Power 750
B. 6-core Power 720 8-core Power 740 16-core Power 770
C. 6-corePower 710 6-core Power 720 8-core Power 740
D. 16-core Power720 16-core Power 760 16-core Power 770
Answer: B

NEW QUESTION: 2
A developer is trying to add a new task profile, using the add button in the Task profiles tab, but does not understand why they cannot use the add button to add a new task profile in Datacap Studio.
What does the developer have to do to succeed in adding a new task profile?
A. Lock the task profiles.
B. Lock the ruleset, and lock the task profiles.
C. Lock the document hierarchy, lock the ruleset, and lock the task profiles.
D. Lock the document hierarchy, and lock the ruleset.
Answer: A

NEW QUESTION: 3
Welches der folgenden BEST fördert die Rechenschaftspflicht der Stakeholder beim Management von Informationssicherheitsrisiken?
A. Festlegung von Sicherheitsbasislinien
B. Etablierung des Informationseigentums
C. Gezielte Sicherheitsverfahren
D. Regelmäßige Überprüfungen auf Nichteinhaltung
Answer: B

NEW QUESTION: 4
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