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NEW QUESTION: 1

A. Option A
B. Option C
C. Option B
D. Option D
Answer: A
Explanation:
AWS CloudWatch supports the custom metrics. The user can always capture the custom data and upload the data to CloudWatch using CLI or APIs. The user can publish data to CloudWatch as single data points or as an aggregated set of data points called a statistic set using the command put-metric-data. It is recommended thatwhen the user is having multiple data points per minute, he should aggregate the data so that it will minimize the number of calls to put-metric-data. In this case it will be single call to CloudWatch instead of 1000 calls if the data is aggregated.

NEW QUESTION: 2
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: 3

A. Option A
B. Option C
C. Option B
D. Option D
Answer: A