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NEW QUESTION: 1
企業は、電子情報開示の要求に効果的に対応できなかった場合の罰則に直面しています。次のうちどれがこの問題から会社への全体的なリスクを減らすことができますか？
A. ファイルシステムの暗号化のみを許可し、個別のファイル暗号化の使用を禁止するポリシーを確立します。
B. エスクローされた既存の企業PKIからの公開鍵を使用するツールのみによる暗号化を許可します。
C. 各ユーザーに、ファイルの暗号化に使用するパスワードを分散リポジトリに記録するように要求します。
D. ドメインパスワードを使用して個々のファイルのみを暗号化し、すべての古いユーザーパスワードをアーカイブすることをユーザーに許可します。
Answer: B
Explanation:
Electronic discovery (also called e-discovery) refers to any process in which electronic data is sought, located, secured, and searched with the intent of using it as evidence in a civil or criminal legal case. E-discovery can be carried out offline on a particular computer or it can be done in a network.
An e-discovery policy would define how data is archived and encrypted. If the data is archived in an insecure manor, a user could be able to delete data that the user does not want to be searched. Therefore, we need to find a way of securing the data in a way that only authorized people can access the data.
A public key infrastructure (PKI) supports the distribution and identification of public encryption keys for the encryption of data. The data can only be decrypted by the private key.
In this question, we have an escrowed corporate PKI. Escrow is an independent and licensed third party that holds something (money, sensitive data etc.) and releases it only when predefined conditions have been met. In this case, Escrow is holding the private key of the PKI.
By encrypting the e-discovery data by using the PKI public key, we can ensure that the data can only be decrypted by the private key held in Escrow and this will only happen when the predefined conditions are met.

NEW QUESTION: 2
As shown in the broadcast network, OSPF runs on four routers and in the same area and on the same network segment. OSPF automatically elects a DR. Multiple BDRs to achieve better backup results.

A. False
B. True
Answer: A

NEW QUESTION: 3
Azure Machine Learning Studioで実験を作成します。 10,000行を含むトレーニングデータセットを追加します。最初の9,000行はクラス0（90パーセント）を表します。
残りの1,000行はクラス1（10パーセント）を表します。
トレーニングセットは、2つのクラス間の不均衡です。 5つのデータ行を使用して、クラス1のトレーニング例の数を4,000に増やす必要があります。 Synthetic Minority Oversampling Technique（SMOTE）モジュールを実験に追加します。
モジュールを構成する必要があります。
どの値を使用する必要がありますか？回答するには、回答領域のダイアログボックス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:

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