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A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df . The pipeline needs to calculate the average humidity and average temperature for each non- overlapping five-minute interval. Events are recorded once per minute per device. Streaming DataFrame df has the following schema: " device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT " Code block: Choose the response that correctly fills in the blank within the code block to complete this task.
Correct Answer: B
This is the correct answer because the window function is used to group streaming data by time intervals. The window function takes two arguments: a time column and a window duration. The window duration specifies how long each window is, and must be a multiple of 1 second. In this case, the window duration is "5 minutes", which means each window will cover a non-overlapping five-minute interval. The window function also returns a struct column with two fields: start and end, which represent the start and end time of each window. The alias function is used to rename the struct column as "time". Verified References: [Databricks Certified Data Engineer Professional], under "Structured Streaming" section; Databricks Documentation , under "WINDOW" section. https://www.databricks.com/blog/2017/05/08/event-time-aggregation- watermarking-apache-sparks-structured-streaming.html