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NEW QUESTION: 1
솔루션 설계자는 온-프레미스 데이터 센터의 Microsoft Windows Server에서 실행되는 레거시 문서 관리 응용 프로그램을 최적화 하기 위해 노력하고 있습니다. 애플리케이션은 네트워크 파일 공유에 많은 수의 파일을 저장합니다. 최고 정보 책임자는 온 프레미스 스토리지를 AWS로 이동하여 온 프레미스 데이터 센터 설치 공간을 줄이고 스토리지 비용을 최소화하고자 합니다. 솔루션 설계자가 이러한 요구 사항을 충족하기 위해 어떻게 해야 합니까?
A. Amazon Elastic Block Store (Amazon EBS) 볼륨을 설정합니다.
B. AWS Storage Gateway 파일 게이트웨이를 설정합니다.
C. Amazon Elastic File System (Amazon EFS) 설정
D. AWS Storage Gateway를 볼륨 게이트웨이로 설정
Answer: B
NEW QUESTION: 2
----
A. Option B
B. Option D
C. Option C
D. Option A
Answer: A
Explanation:
Pre-shared key mode (PSK, also known as Personal mode) is designed for home and small office networks that don't require the complexity of an 802.1X authentication server.[9] Each wireless network device encrypts the network traffic using a 256 bit key. This key may be entered either as a string of 64 hexadecimal digits, or as a passphrase of 8 to 63 printable ASCII characters
NEW QUESTION: 3
Why is configuring regional shared storage locations on a CIFS server using DNS Round Robin the best design choice for vDisk storage?
A. It is the least expensive solution.
B. It creates the least amount of administrative overhead.
C. It is a highly scalable and resilient solution.
D. It reduces the system footprint.
Answer: B
NEW QUESTION: 4
10,000のデータポイントと150の特徴を持つ正規化された数値特徴セットを含むマルチクラス分類タスク用に作成されたデータセットがあります。
データポイントの75%をトレーニングに、25%をテストに使用します。 Pythonでscikit-learn機械学習ライブラリを使用しています。 Xを使用して機能セットを示し、Yを使用してクラスラベルを示します。
次のPythonデータフレームを作成します。
主成分分析(PCA)メソッドを適用して、トレーニングセットとテストセットの両方で、機能セットの次元数を10の機能に減らす必要があります。
コードセグメントをどのように完成させる必要がありますか?回答するには、回答領域で適切なオプションを選択します。
注:それぞれの正しい選択は1ポイントの価値があります。
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