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NEW QUESTION: 1
애플리케이션이 Amazon EC2 인스턴스에서 실행 중입니다. 애플리케이션에 필요한 민감한 정보가 Amazon S3 버킷에 저장됩니다. 버킷에 대한 VPC 액세스 권한만 허용하면서 인터넷 액세스로부터 버킷을 보호해야 합니다.
이를 달성하기 위해 아카이브 된 솔루션이 취해야 하는 조치 조합 ''(2 개 선택)
A. Amazon S3에 대한 VPC 엔드 포인트를 생성합니다.
B. 버킷에서 서버 액세스 로깅 활성화
C. 버킷 정책을 적용하여 S3 엔드 포인트에 대한 액세스를 제한합니다.
D. IAM 정책을 사용하여 사용자가 특정 버킷을 사용하도록 제한
E. 민감한 정보가 있는 버킷에 S3 ACL 추가
Answer: A,C
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 D
B. Option B
C. Option A
D. Option C
Answer: B