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NEW QUESTION: 1
HOTSPOT


Answer:
Explanation:

NEW QUESTION: 2
情報セキュリティプログラムを成功させるには、次のうちどれが最も重要ですか？
A. 新たなセキュリティ技術に関する適切なトレーニング
B. 経営陣のコミットメント
C. 適切なポリシー、標準、手順
D. 主要プロセス所有者とのオープンなコミュニケーション
Answer: B
Explanation:
説明
十分な経営管理サポートは、情報セキュリティプログラムを成功させるための最も重要な要素です。オープンなコミュニケーション、適切なトレーニング、および優れたポリシーと手順は重要ですが、トップマネジメントからのサポートほど重要ではありません。上級管理者のサポートが存在しない場合、彼らは成功を保証しません。

NEW QUESTION: 3
An Amazon S3 bucket in a SysOps administrator account can be accessed by users in other AWS accounts.
How can the Administrator ensure that the bucket is only accessible to members of the Administrator's AWS account?
A. Change the bucket access control list (ACL) to restrict access to the bucket owner
B. Move the S3 bucket from a public subnet to a private subnet in the Amazon VPC
C. Enable server-side encryption for all objects in the bucket
D. Use only Amazon S3 per signed URLs for accessing objects in the bucket
Answer: D

NEW QUESTION: 4
You create an experiment in Azure Machine Learning Studio. You add a training dataset that contains 10,000 rows. The first 9,000 rows represent class 0 (90 percent).
The remaining 1,000 rows represent class 1 (10 percent).
The training set is imbalances between two classes. You must increase the number of training examples for class 1 to 4,000 by using 5 data rows. You add the Synthetic Minority Oversampling Technique (SMOTE) module to the experiment.
You need to configure the module.
Which values should you use? To answer, select the appropriate options in the dialog box in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation

Box 1: 300
You type 300 (%), the module triples the percentage of minority cases (3000) compared to the original dataset (1000).
Box 2: 5
We should use 5 data rows.
Use the Number of nearest neighbors option to determine the size of the feature space that the SMOTE algorithm uses when in building new cases. A nearest neighbor is a row of data (a case) that is very similar to some target case. The distance between any two cases is measured by combining the weighted vectors of all features.
By increasing the number of nearest neighbors, you get features from more cases.
By keeping the number of nearest neighbors low, you use features that are more like those in the original sample.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote