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NEW QUESTION: 1
Contoso Azureでは、次のASP.NETワークロードをホストしています。

販売ソフトウェアのユーザーは、販売時に表示されている在庫と実際の在庫との不一致を報告します。 2つのシステム間でのトランザクションにより、読み取りと書き込みが矛盾します。受注の作成と在庫状況の更新を柔軟なトランザクションでカプセル化します。
トランザクションをサポートするためにコードとデータベースへの変更を推奨する必要があります。
どのアクションをお勧めしますか？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：それぞれ正しい選択は1ポイントの価値があります。

Answer:
Explanation:

NEW QUESTION: 2
For some materials such as titanium, carbon steel and low alloy steel, the number of cycles to fatigue fracture decreases with ________ until an endurance limit is reached. Below this endurance limit, fatigue cracking will not occur, regardless of the number of cycles.
A. Temperature increases
B. Pressure decreases
C. Stress endurance
D. None of the above
Answer: C

NEW QUESTION: 3
Which technology is below the wireless metropolitan area network?
A. WLAN
B. WiMAX
C. Bluetooth
D. 2G/3G
Answer: A

NEW QUESTION: 4
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