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NEW QUESTION: 1
What is the potential negative side effect of performing a forced removal of an intermittently failing disk from a two-way mirrored volume?
A. Volume I/O could degrade significantly.
B. Writes to the volume could be blocked.
C. Any other disk failure could result in loss of data.
D. The volume could be stopped until the disk drive is replaced.
Answer: C

NEW QUESTION: 2
User1 creates table user1 .customers in a partitioned-by-growth (PBG) table space DB1.TS1 Which of the following operations CANNOT be performed by user1 with implicit owner privileges on the table?
A. Run the REORG utility to reorganize the data
B. Issue an SQL LOCK TABLE statement against the customers table
C. Create a VIEW on the customers table
D. Run the LOAD utility to load data
Answer: D

NEW QUESTION: 3
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