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Question 326 - Professional Data Engineer discussion

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You are building a streaming Dataflow pipeline that ingests noise level data from hundreds of sensors placed near construction sites across a city. The sensors measure noise level every ten seconds, and send that data to the pipeline when levels reach above 70 dBA. You need to detect the average noise level from a sensor when data is received for a duration of more than 30 minutes, but the window ends when no data has been received for 15 minutes What should you do?


A.
Use session windows with a 30-mmute gap duration.
Answers
A.
Use session windows with a 30-mmute gap duration.
B.
Use tumbling windows with a 15-mmute window and a fifteen-minute. withAllowedLateness operator.
Answers
B.
Use tumbling windows with a 15-mmute window and a fifteen-minute. withAllowedLateness operator.
C.
Use session windows with a 15-minute gap duration.
Answers
C.
Use session windows with a 15-minute gap duration.
D.
Use hopping windows with a 15-mmute window, and a thirty-minute period.
Answers
D.
Use hopping windows with a 15-mmute window, and a thirty-minute period.
Suggested answer: B

Explanation:

Session windows are dynamic windows that group elements based on the periods of activity. They are useful for streaming data that is irregularly distributed with respect to time. In this case, the noise level data from the sensors is only sent when it exceeds a certain threshold, and the duration of the noise events may vary. Therefore, session windows can capture the average noise level for each sensor during the periods of high noise, and end the window when there is no data for a specified gap duration. The gap duration should be 15 minutes, as the requirement is to end the window when no data has been received for 15 minutes. A 30-minute gap duration would be too long and may miss some noise events that are shorter than 30 minutes. Tumbling windows and hopping windows are fixed windows that group elements based on a fixed time interval. They are not suitable for this use case, as they may split or overlap the noise events from the sensors, and do not account for the periods of inactivity.Reference:

Windowing concepts

Session windows

Windowing in Dataflow

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asked 18/09/2024
Koh Renbin
35 questions
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