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NEW QUESTION: 1
Which two use Oracle dynamic routing gateway (DRG) for connectivity? (Choose two.)
A. Local VCN peering
B. Oracle Cloud Infrastructure FastConnect public peering
C. Remote virtual cloud network (VCN) peering across region
D. Oracle IPsec VPN
Answer: C,D
Explanation:
Explanation
References: https://docs.cloud.oracle.com/en-us/iaas/Content/Network/Concepts/fastconnectoverview.htm

NEW QUESTION: 2
Click the exhibit.

According to the diagram, which Oracle Identity Security Operations Center (SOC) component allows you to monitor Oracle Human Capital Management (HCM) users' activity and access management to avoid service misuse?
A. Oracle Management Cloud
B. Oracle CASB Cloud Service
C. Oracle Database Security
D. Oracle Security Monitoring and Analytics Cloud Service
Answer: D

NEW QUESTION: 3
You administer a Microsoft SQL Server database.
You want to import data from a text file to the database.
You need to ensure that the following requirements are met:
Data import is performed by using a stored procedure.
Data is loaded as a unit and is minimally logged.
Which data import command and recovery model should you choose? (To answer, drag the appropriate data import command or recovery model to the appropriate location or locations in the answer area. Each data import command or recovery model may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.)
Select and Place:

Answer:
Explanation:

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。
どのようにコードセグメントを完成させるべきですか？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:

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.
参照：
https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html