Valid Databricks-Certified-Professional-Data-Engineer Dumps shared by EduDump.com for Helping Passing Databricks-Certified-Professional-Data-Engineer Exam! EduDump.com now offer the newest Databricks-Certified-Professional-Data-Engineer exam dumps, the EduDump.com Databricks-Certified-Professional-Data-Engineer exam questions have been updated and answers have been corrected get the newest EduDump.com Databricks-Certified-Professional-Data-Engineer dumps with Test Engine here:
A facilities-monitoring team is building a near-real-time Power BI dashboard off the Delta table device_readings : * device_id STRING - unique sensor ID * event_ts TIMESTAMP - ingestion timestamp (UTC) * temperature_c DOUBLE - temperature in °C * notes STRING For each sensor, the team needs one row per non-overlapping 5-minute interval, offset by 2 minutes (for example, intervals like 00:02-00:07 , 00:07-00:12 , and so on), showing the average temperature in that slice. The result must include each interval's start and end timestamps so downstream tools can plot time-series bars correctly. Which query satisfies the requirement?
Correct Answer: A
Spark documents window(timeColumn, windowDuration, slideDuration=None, startTime=None) for time bucketing. The startTime argument is specifically the offset from the epoch used to align window boundaries, and the output is a window struct with start and end fields. That exactly matches the requirement for 5-minute non-overlapping intervals offset by 2 minutes. ( Apache Spark ) Option A correctly uses window(event_ts, ' 5 minutes ' , ' 5 minutes ' , ' 2 minutes ' ) , which creates tumbling 5-minute windows offset by 2 minutes and then exposes win.start and win.end . Option B is malformed in how it references the generated window column, option C creates minute-aligned groupings rather than true 5- minute tumbling windows, and option D computes a rolling window average instead of one row per non- overlapping time bucket. ( Apache Spark )