Practice SQL with
real company scenarios.

For each video, calculate the percentage of viewers who watched until the end out of all viewers who started the video. Round the result to 2 decimal places.

For each store in zepto_daily_sales, excluding any row where revenue is NULL (the store was closed, not a zero-revenue day), compute a 3-day moving average of revenue ordered by sale_date — the average of the current trading day and the two trading days immediately before it (fewer days if not enough history exists yet). Return store_name, sale_date, revenue, and moving_avg_3day rounded to 2 decimal places, sorted by store_name and sale_date.

For each account in zerodha_clients where account_type is 'corporate', return client_name and email for accounts that either use a personal email domain (gmail.com, yahoo.com, or hotmail.com) or have no email on file at all. Sort by client_name.

Find every restaurant that has never had a cancelled order — meaning it has zero orders with status = 'cancelled'. This includes restaurants with no orders at all, since they trivially satisfy 'never cancelled.' Use EXISTS or NOT EXISTS with a correlated subquery rather than aggregating and filtering afterward. Return restaurant_name and city, sorted alphabetically by restaurant_name.