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NEW QUESTION: 1
ABC.com wants to have 16 accounting periods that include an adjusting period for each quarter in its 4-5-4 accounting calendar. Its fiscal year end is March 31. The normal month end is on a Friday, but the last day of each quarter is always on the last day of the quarter's final calendar month. Choose the remaining setup for the calendar to meet the requirement.
A. Define 12 monthly periods based on the 4-5-4 calendar ending on the last day of each month. Define an adjusting period on the last day of each quarter.
B. Define 12 monthly periods based on the 4-5-4 calendar ending on the last Friday of each month. Define an adjusting period on the last Friday of each quarter.
C. Define 12 monthly periods based on the 4-5-4 calendar ending on the last Friday of each month. Define four adjusting periods for March 31 of the fiscal year.
D. Define 12 monthly periods based on the 4-5-4 calendar ending on the last Friday of each month, except for the last month of each quarter that falls on the last day of the month. Define four adjusting periods on March 31 of the fiscal year.
E. Define 12 monthly periods based on the 4-5-4 calendar ending on the last Friday of each month, except for the last month of each quarter ending on the last day of the month. Define an adjusting period for each of the last day of the quarter.
Answer: E
NEW QUESTION: 2
展示を参照してください。
ルーティングプロトコルコードDは出力でどのルートタイプを表しますか?
A. 静的に割り当てられたルート
B. 内部BGPルート
C. ローカルに構成されたIPの/ 24ルート
D. EIGRPを通じて学習したルート
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
Which models are ICT gateway. (Multiple Choice)
A. AR3600 Series
B. ARS10 Series
C. AR160-M Series
Answer: A,B,C