Can you please help on below points ?

1. How do you decide on the job vs interactive cluster confusion ? I have a scenario where we have a hourly job that almost runs for more than 35 minutes. We have other jobs that run for a much smaller time. In such cases how would you decide between them and is it still cheaper to go for separate job clusters in such a case ?
2. Is there a standard way to check how much peak memory a job uses ? For example, one of the jobs I have come across has a source data of around 700 mb, references DBMS tables as views (Which I assume is loaded into memory ? Can anyone confirm ?) and has some looping ETL logic written in pyspark...in such a case what would be the ideal way to identify how much peak memory this job uses and then create a cluster based on that ?