Google Cloud Certified Associate Data Practitioner Practice Mode

Community Discussion

Written by other learners. Comments can disagree with the marked answer — check the official documentation when in doubt.

• Chose C1

  partition by store id to get each store seperatly and then order by date and create a week window and then compute the averaging of sales in that window

• Chose C1

  Should be partitioned by store id to get the store based sales, also, to check the weekly average sales, the ROWS between 6 preceeding and current row function will cater to it.

• Chose C1

  The best option is C. Option C is best because it correctly partitions by store_id to calculate the moving average for each store individually, and orders by date to ensure the moving average is calculated chronologically over the preceding 7 days (including the current day, thus 6 preceding). ROWS BETWEEN 6 PRECEDING AND CURRENT ROW then correctly defines the 7-day window for the average. Option A is incorrect because it orders by total_sales instead of date, making the rolling average based on sales value order, not time, which is illogical for a weekly trend. Option B is incorrect because it partitions by date instead of store_id, calculating a daily moving average across all stores, not per store. Option D is incorrect because it partitions by total_sales, which is nonsensical for analyzing trends by location, and orders by date within that illogical partition. Therefore, Option C is the only query that correctly calculates a weekly moving average of sales by store location.