32. Unpopular movies

hard

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Table 1: film


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Table 2: inventory

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Table 3: rental


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Sample results




Last Submission postgres

Expected results



More IN, BETWEEN, LIKE, CASE WHEN questions

ID Title Level FTPR
178 Members who worked at both Microsoft and Google linkedin medium
34%
177 Purchases by platform report amazon medium
10%
176 Employees' annual bonus amazon easy
12%
160 Sellers with no sales by day ebay hard
11%
156 Cancellation rate by unbanned users lyft hard
15%
155 Driver with the highest cancellation rate lyft easy
24%
151 Salary report dropbox easy
18%
138 Happy restaurants doordash easy
14%
136 Extremely late orders doordash easy
11%
131 Churned accounts affirm hard
10%
102 Histogram by visit session duration google hard
17%
35 Film length report easy
27%
34 Stocked up movies easy
27%
33 Returning customers medium
8%
31 Movies that have not been returned easy
26%
30 Inactive customers in May easy
22%
29 Second highest spend customer medium
14%
28 Film with the second largest cast medium
26%
27 Film with the largest cast easy
27%
26 Second shortest film easy
30%
25 Shortest film easy
46%
24 Films with more than 10 actors medium
19%
22 Average cost per rental transaction easy
42%
15 Fast movie watchers vs slow watchers hard
6%
14 Good days and bad days hard
4%
13 Actors' first name medium
14%
12 Actors' last name ending in 'EN' or 'RY' easy
27%
11 Actors' last name easy
21%