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Vendor: SolarWinds

Exam Code: Hybrid-Cloud-Observability-Network-Monitoring Dumps

Questions and Answers: 104

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NEW QUESTION: 1
ユーザーはネットワークサービスにはアクセスできませんが、インターネットにはアクセスできます。技術者は、ネットワークが10.10.10.0の場合、コンピューターのIPアドレスが192.168.1.144であることを認識しています。次のうちどれが問題である可能性が最も高いですか?
A. 重複したIPアドレス
B. サーバー権限の変更
C. 不適切なゲートウェイ
D. 不正なDHCPサーバー
Answer: D

NEW QUESTION: 2
Which multicast routing protocol supports dense mode, sparse mode and bidirectional
mode?
A. MOSPF
B. DVMRP
C. MSDP
D. PIM
E. MP-BGP
Answer: D

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。
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

NEW QUESTION: 4
Which of the following statements about an attribute is NOT TRUE?
A. It can display date and time data.
B. It can be hidden on the chart.
C. It is used in complex searches.
D. It prevents duplicate entities from being added.
Answer: D


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