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NEW QUESTION: 1
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新しいプロジェクトのリソースを展開するために利用できるApp Serviceプランのセットがあります。利用可能なサービス層は、サービス層の展示に表示されます。 （[サービス層]タブをクリックします。）

プロジェクト展示に示されているように、プロジェクトのリソースをプロビジョニングする必要があります。 （[プロジェクト]タブをクリックします。）

Adventure Works aプロジェクトでは、展開スロットの展示に示されている展開スロットを使用する必要があります。 （[展開スロット]タブをクリックします。）

各プロジェクトのリソースを展開する場所を決定する必要があります。
以下の各ステートメントについて、ステートメントが真である場合は「はい」を選択します。それ以外の場合は、「いいえ」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:

説明

NEW QUESTION: 2
Failure in which of the following testing stages would have the GREATEST impact on the implementation of new application software?
A. Integration testing
B. System testing
C. Unit testing
D. Acceptance testing
Answer: D
Explanation:
Acceptance testing is the final stage before the software is installed and is available for use. The greatest impact would occur if the software fails at the acceptance testing level, as this could result in delays and cost overruns. System testing is undertaken by the developer team to determine if the software meets user requirements per specifications. Integration testing examines the units/modules as one integrated system and unit testing examines the individual units or components of the software. System, integration and unit testing are all performed by the developers at various stages of development; the impact of failure is comparatively less for each than failure at the acceptance testing stage.

NEW QUESTION: 3



RMAN> BACKUP DATABASE PLUS ARCHIVELOG DELETE INPUT;

A. Option B
B. Option A
C. Option C
D. Option D
Answer: A
Explanation:
References:
https://docs.oracle.com/cd/B13789_01/server.101/b10734/rcmbackp.htm

NEW QUESTION: 4
You are using C-Support Vector classification to do a multi-class classification with an unbalanced training dataset. The C-Support Vector classification using Python code shown below:

You need to evaluate the C-Support Vector classification code.
Which evaluation statement should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation:
Box 1: Automatically adjust weights inversely proportional to class frequencies in the input data The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as n_samples / (n_classes * np.bincount(y)).
Box 2: Penalty parameter
Parameter: C : float, optional (default=1.0)
Penalty parameter C of the error term.
References:
https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html