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NEW QUESTION: 1
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: 2
You are configuring VXLAN on a network of QFX5100s, and are using the set vlans vlan100 vxlan unreachable-vtep-aging-timer 600 command.
What does this command do?
A. This command ages out a remotely learned VTEP address after 600 seconds of inactivity.
B. This command ages out a locally learned MAC address assigned to the VTEP after 600 seconds of inactivity.
C. This command ages out the address of the remote VTEP 600 seconds after the last learned MAC address expires.
D. This command ages out all learned MAC addresses assigned to the VTEP after 600 seconds of inactivity.
Answer: C

NEW QUESTION: 3
사용량이 많지 않은 시간에 개발자는 일반 작업 부하에 영향을 주지 않으면 서 전체 Amazon DynamoDB 테이블 검색의 실행 시간을 최소화하려고 합니다. 작업량은 비 피크 시간 동안 강하게 일관성 있는 읽기 용량 단위의 평균을 나타냅니다.
개발자가이 스캔을 어떻게 최적화합니까?
A. 스캔 작업 중 읽기 용량 단위 늘리기
B. 스캔 작업 중에 일관성을 유지하도록 일관성을 변경합니다.
C. 비율을 제한하면서 병렬 스캔 사용
D. 순차적 스캔 사용
Answer: C
Explanation:
설명
병렬 스캔은 스캔 성능을 향상시키는 데 도움이되며 제한 매개 변수를 사용하면 제공된 처리량이 제한 될 수 있습니다.
AWS 설명서 참조 - DynamoDB 병렬 스캔
기본적으로 스캔 작업은 데이터를 순차적으로 처리합니다. DynamoDB는 데이터를 1MB 단위로 응용 프로그램에 반환하고 응용 프로그램은 다음 1MB의 데이터를 검색하기 위해 추가 작업을 수행합니다.
스캔 할 테이블이나 인덱스가 클수록 테이크 시간이 길어집니다. 또한 순차적 검색은 준비된 읽기 처리량을 항상 충분히 활용하지 못할 수 있습니다. DynamoDB가 여러 물리적 파티션에 큰 테이블의 데이터를 배포하더라도 스캔 작업은 한 번에 하나의 파티션 만 읽을 수 있습니다. 이러한 이유 때문에 a의 처리량은 단일 파티션의 최대 처리량으로 인해 제한됩니다.
이러한 문제를 해결하기 위해 스캔 작업은 표 또는 보조 색인을 논리적으로 여러 세그먼트로 나눌 수 있으며 여러 응용 프로그램 작업자가 세그먼트를 병렬로 검색합니다. 각 작업자는 스레드 (멀티 스레딩을 지원하는 프로그래밍 언어에서) 또는 운영 체제 프로세스가 될 수 있습니다.
다수의 작업자가 있는 병렬 스캔은 스캔중인 테이블 또는 인덱스에 대해 프로비저닝 된 모든 처리량을 쉽게 소비 할 수 있습니다. 테이블 또는 색인이 다른 응용 프로그램에서 많은 읽기 또는 쓰기 활동을 초래할 경우 이러한 스캔을 피하는 것이 가장 좋습니다.
요청 당 반환되는 데이터의 양을 제어하려면 Limit 매개 변수를 사용하십시오. 이렇게하면 한 명의 작업자가 다른 모든 작업자를 희생시키면서 모든 처리량을 소비하는 상황을 방지 할 수 있습니다.