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NEW QUESTION: 1
マネーロンダリング防止のリスク評価はいつ更新されますか？
A. 取締役会が変更されたとき
B. 合併または買収後
C. 2年ごと
D. Financial Action Task Forceから指示された場合
Answer: B

NEW QUESTION: 2
ドラッグドロップ
サイト間VPNを使用してAzureに接続する予定のオンプレミスネットワークがあります。
Azureには、10.10.0.0 / 16のアドレススペースを使用するVNet1という名前のAzure仮想ネットワークがあります。 VNet1には、10.0.0.0 / 24のアドレススペースを使用するSubnet1という名前のサブネットが含まれています。
Azureへのサイト間VPNを作成する必要があります。
どの4つのアクションを順番に実行する必要がありますか？回答するには、適切なアクションをアクションのリストから回答エリアに移動し、正しい順序に並べます。
注：回答の選択肢の複数の順序が正しいです。選択した正しい注文のいずれかのクレジットを受け取ります。

Answer:
Explanation:

Note: More than one order of answer choices is correct.
Creating a local gateway (a logical object that represents the on-premise router) can be done at step 1, step 2 or step 3. The other three steps must be done in order: create gateway subnet then create VPN gateway then create the VPN connection. The VPN connection is a connection between the VPN gateway and the Local gateway.

NEW QUESTION: 3
A medical record filing system for a government medical fund is using an Amazon S3 bucket to archive documents related to patients. Every patient visit to a physician creates a new file, which can add up to millions of files each month. Collection of these files from each physician is handled via a batch process that runs every night using AWS Data Pipeline. This is sensitive data, so the data and any associated metadata must be encrypted at rest.
Auditors review some files on a quarterly basis to see whether the records are maintained according to regulations. Auditors must be able to locate any physical file in the S3 bucket or a given data, patient, or physician. Auditors spend a signification amount of time locating such files.
What is the most cost-and time-efficient collection methodology in this situation?
A. Use Amazon S3 event notifications to populate an Amazon DynamoDB table with metadata about every file loaded to Amazon S3, and partition them based on the month and year of the file.
B. Use Amazon API Gateway to get the data feeds directly from physicians, batch them using a Spark application on Amazon Elastic MapReduce (EMR), and then store them in Amazon S3 with folders separated per physician.
C. Use Amazon S3 event notifications to populate and Amazon Redshift table with metadata about every file loaded to Amazon S3, and partition them based on the month and year of the file.
D. Use Amazon kinesis to get the data feeds directly from physician, batch them using a Spark application on Amazon Elastic MapReduce (EMR) and then store them in Amazon S3 with folders separated per physician.
Answer: C