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NEW QUESTION: 1
あなたの会社は大量の航空画像データをS3にアップロードしました。これまで、オンプレミス環境では、専用のサーバーグループを使用してこのデータを処理し、Rabbit MQ-ジョブ情報を取得するオープンソースメッセージングシステムを使用していましたサーバーに。処理されたデータはテープに送られ、オフサイトに出荷されます。マネージャーは、現在の設計を維持し、AWSアーカイブストレージとメッセージングサービスを活用してコストを最小化するように指示しました。
どちらが正しい？
A. S3オブジェクトのストレージクラスを低冗長化ストレージに変更します。スポットインスタンスを使用してSQSでメッセージを処理するキュー深度によってトリガーされる自動スケーリングワーカーをセットアップします。データが処理されたら、S3オブジェクトのストレージクラスをGlacierに変更します。
B. SNSを使用してジョブメッセージを渡します。CloudWatchアラームを使用して、アイドルになったスポットワーカーインスタンスを終了します。データが処理されたら、S3オブジェクトのストレージクラスをGlacierに変更します。
C. ジョブメッセージの受け渡しにSQSを使用し、Cloud Watchアラームを使用して、EC2ワーカーインスタンスがアイドル状態になったときに終了します。データが処理されたら、S3オブジェクトのストレージクラスを低冗長化ストレージに変更します。
D. スポットインスタンスを使用してSOSでメッセージを処理するキュー深度によってトリガーされる自動スケーリングワーカーをセットアップします。データが処理されると、
Answer: B

NEW QUESTION: 2
An administrator deployed an IBM WebSphere MQ application on an AIX server which is causing a lot of errors to be generated in the WebSphere MQ error logs. Which option should be used by the administrator to increase the size of the IBM WebSphere MQ error logs?
A. Recreate the queue manager specifying the new error log size as a parameter to crtmqm
B. Use the MQSC command ALTER QMGR
C. Set the environment variable AMQ_ERROR_LOG to the new size and recreate the queuemanager
D. Use the QMErrorLog stanza in qm.ini
Answer: D

NEW QUESTION: 3
HOTSPOT
A company develops a moisture sensor that integrates with Azure. You are designing a solution that collects data from the sensor devices and analyzes the data.
You need to design the architecture for the solution.
Which technology should you recommend? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point

Answer:
Explanation:

Explanation:

Box 1: Azure IoT Edge
Azure IoT Edge is made up of three components:
IoT Edge modules are containers that run Azure services, 3rd party services, or your own code. They are deployed to IoT Edge devices and execute locally on those devices.
The IoT Edge runtime runs on each IoT Edge device and manages the modules deployed to each device.
A cloud-based interface enables you to remotely monitor and manage IoT Edge devices.
Box 2: Azure Stream Analytics
Azure Stream Analytics on IoT Edge empowers developers to deploy near-real-time analytical intelligence closer to IoT devices so that they can unlock the full value of device- generated data.
With Azure Streaming Analytics (ASA) on IoT Edge, enterprises benefit from running Complex Event Processing (CEP) closer to where the data is produced. For example, manufacturing safety systems are required to respond to operational data with ultra-low latency. With ASA on IoT Edge, you can analyze sensor data in near real time and issue commands when you detect anomalies to stop a machine or trigger alerts.
Box 3: Azure IoT Hub
IoT Hub is a managed service, hosted in the cloud, that acts as a central message hub for bi-directional communication between your IoT application and the devices it manages.
You can use Azure IoT Hub to build IoT solutions with reliable and secure communications between millions of IoT devices and a cloud-hosted solution backend. You can connect virtually any device to IoT Hub.