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NEW QUESTION: 1
Given the raw data file YEARAMT:
----|---10---|---20---|----30
1901 2
1905 1
1910 6
1925 .
1941 1
The following SAS program is submitted:
data coins;
infile 'yearamt';
input year quantity;
<insert statement(s) here>
run;
Which statement(s) completed the program and produced a non-missing value for the variable
TOTQUANTITY in the final observation of the output data set?
A. totquantity + quantity;
B. retain totquantity; totquantity = totquantity + quantity;
C. totquantity = sum(totquantity + quantity);
D. retain totquantity0; totquantity = totquantity + quantity;
Answer: A

NEW QUESTION: 2
The single most important consideration to make when developing your security program, policies, and processes is:
A. Streamlining for efficiency
B. Establishing your authority as the Security Executive
C. Budgeting for unforeseen data compromises
D. Alignment with the business
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
A university wants to deploy ClearPass with the Guest module. The university has two types of users that need to use web login authentication The first type of users are students whose accounts are in an Active Directory server The second type of users ace friends of students who need to self-register to access the network
How should the service be set up in the Policy Manager for this network?
A. Guest User Repository as the authentication source and the Active Directory server as the authentication source
B. Guest user Repository and ActiveDirectory server both as authentication sources
C. Active Directory server as the authentication source and the Guest User Repository as the authorization source
D. either the Guest User Repository or Active Directory server should be the single authentication source
E. Guest User Repository as the authentication source, and Guest User Repository and
Active Directory server as authentication source
Answer: E