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NEW QUESTION: 1
アプリケーションは、EC2インスタンスに接続されたEBSボリュームに機密情報を保存します。どうすれば情報を保護できますか？以下のオプションから2つの回答を選択してください
A. 暗号化された新しいAmazon EBSボリュームを作成してマウントします。データを新しいボリュームに移動します。古いAmazon EBSボリュームを削除します* t
B. EBSボリュームをアンマウントし、スナップショットを作成して、スナップショットを暗号化します。 Amazon EBSボリュームを再マウントします
C. EBSボリュームを暗号化することはできません。暗号化のためにライフサイクルポリシーを使用してS3にデータを転送する必要があります。
D. 暗号化されていないスナップショットをコピーし、チェックボックスをオンにして新しいスナップショットを暗号化します。この暗号化されたスナップショットから復元されたボリュームも暗号化されます。
Answer: A,D
Explanation:
Explanation
These steps are given in the AWS documentation
To migrate data between encrypted and unencrypted volumes
1) Create your destination volume (encrypted or unencrypted, depending on your need).
2) Attach the destination volume to the instance that hosts the data to migrate.
3) Make the destination volume available by following the procedures in Making an Amazon EBS Volume Available for Use. For Linux instances, you can create a mount point at /mnt/destination and mount the destination volume there.
4) Copy the data from your source directory to the destination volume. It may be most convenient to use a bulk-copy utility for this.
To encrypt a volume's data by means of snapshot copying
1) Create a snapshot of your unencrypted CBS volume. This snapshot is also unencrypted.
2) Copy the snapshot while applying encryption parameters. The resulting target snapshot is encrypted.
3) Restore the encrypted snapshot to a new volume, which is also encrypted.
For more information on EBS Encryption, please refer to the below document link: from AWS
* http://docs.aws.a
mazon.com/AWSEC2/latest/UserGuide/ EBSEncryption.html

NEW QUESTION: 2
Which of the following pseudocodes can be used to handle program exceptions?
A. If program detects another instance of itself, then kill program instance.
B. If user enters invalid input, then restart program.
C. If program module crashes, then restart program module.
D. If user's input exceeds buffer length, then truncate the input.
Answer: C

NEW QUESTION: 3
あなたはホテル予約のウェブサイト会社で働いているデータサイエンティストです。 Azure Machine Learningサービスを使用して、不正なトランザクションを識別するモデルをトレーニングします。
Azure Machine Learning SDKのModel.deployメソッドを使用して、モデルをAzure Machine LearningリアルタイムWebサービスとしてデプロイする必要があります。デプロイされたWebサービスは、トランザクションデータ入力に基づいて不正のリアルタイム予測を返す必要があります。
モデルのデプロイに使用されるInferenceConfigクラスのentry_scriptパラメーターとして指定されるスクリプトを作成する必要があります。
入力スクリプトは何をすべきですか？
A. モデルを読み込み、それを使用して入力データからラベルを予測します。
B. 適切なタグとプロパティを使用してモデルを登録します。
C. Webサービスがデプロイされている推論クラスターでノードを起動します。
D. Webサービスコンピューティング用のConda環境を作成し、必要なPythonパッケージをインストールします。
E. 推論の計算に必要なコアの数とメモリの量を指定します。
Answer: A
Explanation:
The entry script receives data submitted to a deployed web service and passes it to the model. It then takes the response returned by the model and returns that to the client. The script is specific to your model. It must understand the data that the model expects and returns.
The two things you need to accomplish in your entry script are:
Loading your model (using a function called init())
Running your model on input data (using a function called run())
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-and-where