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NEW QUESTION: 1
You have an Azure Active Directory tenant named Contoso.com that includes following users:

Contoso.com includes following Windows 10 devices:

You create following security groups in Contoso.com:

For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

References:
https://docs.microsoft.com/en-us/azure/active-directory/devices/overview

NEW QUESTION: 2

どの操作が展示に表されていますか?
A. ライトバックキャッシュ
B. ライトスルーキャッシュ
C. キャッシュミスの読み取り
D. キャッシュヒットの読み取り
Answer: A

NEW QUESTION: 3
10,000個のデータポイントと150個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フィーチャセットの次元を10フィーチャ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、回答エリア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には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

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