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NEW QUESTION: 1
Which two statements about the DES algorithm are true? (Choose two)
A. The DES algorithm uses a 56-bit key.
B. The DES algorithm is based on symmetric cryptography.
C. The DES algorithm encrypts a block of 128 bits.
D. The DES algorithm is based on asymmetric cryptography.
E. The DES algorithm is a stream cipher.
Answer: A,B

NEW QUESTION: 2
注：この質問は、同じシナリオを提示する一連の質問の一部です。シリーズの各質問には、述べられた目標を達成する可能性のある独自の解決策が含まれています。一部の質問セットには複数の正しい解決策がある場合がありますが、他の質問セットには正しい解決策がない場合があります。
このセクションの質問に回答した後は、その質問に戻ることはできません。その結果、これらの質問はレビュー画面に表示されません。
Azure StorageBLOBデータを処理するためのHTTPトリガーAzureFunctionアプリを開発します。アプリは、blobの出力バインディングを使用してトリガーされます。
アプリは4分後もタイムアウトし続けます。アプリはBLOBデータを処理する必要があります。
アプリがタイムアウトせず、BLOBデータを処理しないようにする必要があります。
解決策：App Serviceホスティングプランを使用するようにアプリを構成し、AlwaysOn設定を有効にします。
ソリューションは目標を達成していますか？
A. はい
B. いいえ
Answer: B
Explanation:
Explanation
Instead pass the HTTP trigger payload into an Azure Service Bus queue to be processed by a queue trigger function and return an immediate HTTP success response.
Note: Large, long-running functions can cause unexpected timeout issues. General best practices include:
Whenever possible, refactor large functions into smaller function sets that work together and return responses fast. For example, a webhook or HTTP trigger function might require an acknowledgment response within a certain time limit; it's common for webhooks to require an immediate response. You can pass the HTTP trigger payload into a queue to be processed by a queue trigger function. This approach lets you defer the actual work and return an immediate response.
Reference:
https://docs.microsoft.com/en-us/azure/azure-functions/functions-best-practices

NEW QUESTION: 3
You have a C# application that process data from an Azure IoT hub and performs complex transformations.
You need to replace the application with a real-time solution. The solution must reuse as much code as possible from the existing application.
A. Azure Stream Analytics
B. Azure Event Grid
C. Azure Databricks
D. Azure Data Factory
Answer: A
Explanation:
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. UDF are available in C# for IoT Edge jobs Azure Stream Analytics on IoT Edge runs within the Azure IoT Edge framework. Once the job is created in Stream Analytics, you can deploy and manage it using IoT Hub.
References:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-edge

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