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NEW QUESTION: 1
A. Option A
B. Option B
Answer: A
Explanation:
If the INSERT INTO statement raises an error, the statement will be caught and an error
51000 will be thrown. In this case no records will have been inserted.
Note:
You can implement error handling for the INSERT statement by specifying the statement in a TRY...CATCH construct.
If an INSERT statement violates a constraint or rule, or if it has a value incompatible with the data type of the column, the statement fails and an error message is returned.
References: https://msdn.microsoft.com/en-us/library/ms174335.aspx
NEW QUESTION: 2
You need to create a query that identifies the trending topics.
How should you complete the query? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
From scenario: Topics are considered to be trending if they generate many mentions in a specific country during a 15-minute time frame.
Box 1: TimeStamp
Azure Stream Analytics (ASA) is a cloud service that enables real-time processing over streams of data flowing in from devices, sensors, websites and other live systems. The stream-processing logic in ASA is expressed in a SQL-like query language with some added extensions such as windowing for performing temporal calculations.
ASA is a temporal system, so every event that flows through it has a timestamp. A timestamp is assigned automatically based on the event's arrival time to the input source but you can also access a timestamp in your event payload explicitly using TIMESTAMP BY:
SELECT * FROM SensorReadings TIMESTAMP BY time
Box 2: GROUP BY
Example: Generate an output event if the temperature is above 75 for a total of 5 seconds SELECT sensorId, MIN(temp) as temp FROM SensorReadings TIMESTAMP BY time GROUP BY sensorId, SlidingWindow(second, 5) HAVING MIN(temp) > 75 Box 3: SlidingWindow Windowing is a core requirement for stream processing applications to perform set-based operations like counts or aggregations over events that arrive within a specified period of time. ASA supports three types of windows: Tumbling, Hopping, and Sliding.
With a Sliding Window, the system is asked to logically consider all possible windows of a given length and output events for cases when the content of the window actually changes - that is, when an event entered or existed the window.
NEW QUESTION: 3
会社がビデオ処理プラットフォームを実行しています。ファイルは、Amazon EFS共有に保存するウェブサーバーに接続するユーザーによってアップロードされます。このWebサーバーは、単一のAmazon EC2インスタンスで実行されています。 Auto Scalingグループで実行されている別のインスタンスのグループは、処理する新しいファイルのEFS共有ディレクトリ構造をスキャンし、一緒にアップロードされた指示ファイルに従って新しいビデオ（サムネイル、異なる解像度、圧縮など）を生成しますビデオファイル。 Auto Scalingグループによって管理されているインスタンスのグループで実行されている別のアプリケーションがビデオファイルを処理し、EFS共有からそれらを削除します。結果はS3バケットに保存されます。処理されたビデオファイルへのリンクが顧客にメールで送信されます。
同社は最近、Auto Scalingグループにインスタンスを追加すると、多くのファイルが2回処理されるため、画像処理速度が向上しないことを発見しました。これらのビデオファイルの最大サイズは
2GB。
信頼性を向上させ、ビデオファイルの冗長処理を減らすために、ソリューションアーキテクトは何をすべきですか
A. EFS共有のコンテンツをAmazon S3に同期するために、Webサーバーインスタンスでcronジョブをセットアップします。ファイルがアップロードされるたびにAWS Lambda関数をトリガーして、ビデオファイルを処理し、Amazon S3に結果を保存します。 Amazon CloudWatchイベントを使用すると、Amazon SESジョブがトリガーされ、処理されたファイルへのリンクを含むEメールが顧客に送信されます。
B. Amazon S3から実行するようにWebアプリケーションを書き換え、ビデオファイルをS3バケットにアップロードします。新しいファイルがアップロードされるたびに、AWS Lambda関数をトリガーして、リンクと指示を含むSQSキューにメッセージを書き込みます。 SQSキューとS3バケットから読み取るようにビデオ処理アプリケーションを変更します。キュー深度メトリックスを使用して、ビデオ処理インスタンスのAuto Scalingグループのサイズを調整します。
C. Amazon S3から直接実行するようにWebアプリケーションを書き換え、Amazon API Gatewayを使用してビデオファイルをS3バケットにアップロードします。新しいビデオファイルを処理して別のバケットに保存するためにファイルがアップロードされるたびに、S3トリガーを使用してAWS Lambda関数を実行します。 CloudWatchイベントを使用して、SESジョブをトリガーし、処理されたファイルへのリンクを含むEメールを顧客に送信します。
D. ウェブアプリケーションを変更して、ビデオファイルを直接Amazon S3にアップロードします。 Amazon CloudWatchイベントを使用して、ファイルがアップロードされるたびにAWS Lambda関数をトリガーし、このLambda関数にメッセージをAmazon SQSキューに入れさせます。新しいファイルのSQSキューから読み取るようにビデオ処理アプリケーションを変更し、キューの深さメトリックを使用してビデオ処理Auto Scalingグループのインスタンスをスケーリングします。
Answer: D