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NEW QUESTION: 1
Susan sends an email to Paul. Who determines the meaning and the value of information in this email?
A. Susan, the sender of the information.
B. Paul and Susan, the sender and the recipient of the information.
C. Paul, the recipient of the information.
Answer: C

NEW QUESTION: 2
自動スケーリングを使用して新しいインスタンスを起動するアプリケーションをAWSにデプロイしました。ここで、新しいインスタンスのインスタンスタイプを変更します。この展開を達成するためのアクションアイテムの1つは次のうちどれですか。
A. 新しいインスタンスタイプで新しいEC2インスタンスを作成し、自動スケーリンググループにアタッチします
B. Cloudformationを使用して、新しいインスタンスタイプで新しいアプリケーションをデプロイします
C. 新しいインスタンスタイプで新しい起動構成を作成します
D. Elastic Beanstalkを使用して、新しいインスタンスタイプで新しいアプリケーションをデプロイします
Answer: C
Explanation:
The ideal way is to create a new launch configuration, attach it to the existing Auto Scaling group, and terminate the running instances.
Option A is invalid because Clastic beanstalk cannot launch new instances on demand. Since the current scenario requires Autoscaling, this is not the ideal option Option B is invalid because this will be a maintenance overhead, since you just have an Autoscaling Group. There is no need to create a whole Cloudformation template for this.
Option D is invalid because Autoscaling Group will still launch CC2 instances with the older launch configuration For more information on Autoscaling Launch configuration, please refer to the below document link:
http://docs.aws.amazon.com/autoscaling/latest/userguide/l_a unchConfiguration.html

NEW QUESTION: 3


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.