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NEW QUESTION: 1
Which of the following messages are exchanged between the MME and the SGW during the dedicated bearer activation procedure?
A. Session Management Request / Session Management Response
B. RA-Request / RA-Answer
C. Attach Request / Attach Accept
D. Create Bearer Request / Create Bearer Response
Answer: D
Explanation:
Reference:
https://www.sciencedirect.com/topics/engineering/dedicated-bearer
NEW QUESTION: 2
Universal containers is implementing salesforce knowledge and immediately wants to begin building a repository of frequently asked questions(FAQ) encountered by contact center agents. How can this be accomplished?
A. Create an FAQ article type and enable the submit articles feature on the case close page layout.
B. Define a data category called FAQ and assign category visibility to users in the contact centre role.
C. Enable ideas for contact center agents and have them submit FAQ articles at the time a case is closed.
D. Create an FAQ article type and configure the enable suggested articles option in support settings.
Answer: A
NEW QUESTION: 3
Your organization has developed and deployed several Azure App Service Web and API applications. The applications use Azure Key Vault to store several authentication, storage account, and data encryption keys. Several departments have the following requests to support the applications:
You need to recommend the appropriate Azure service for each department request.
What should you recommend? To answer, configure the appropriate options in the dialog box in the answer area.
NOTE: Each correct selection is worth one point.
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％をトレーニングに使用し、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。
トレーニングセットとテストセットの両方で、機能セットの次元を10個の機能に減らすには、主成分分析（PCA）メソッドを適用する必要があります。
コードセグメントをどのように完了す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：正しい選択はそれぞれ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
