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FreePro125 ProblemsSQL + Python

Streaming Media SQL & Python Interview Questions

Streaming platforms optimize content recommendations, analyze viewing behavior, and track subscription retention. These SQL and Python challenges are modeled after work at Netflix, Spotify, YouTube, Disney+, Max, Apple TV+, Amazon Prime Video, Paramount+, Twitch, Tidal, and more. Practice watch time analysis, content completion rates, content ROI, catalog performance, and subscriber churn modeling.

Streaming Media

125 total problems

SQL60
Python65
Top Companies Hiring in Streaming Media

Questions are relevant for real analytics problems data science teams solve at these companies.

Netflix
Netflix
Spotify
Spotify
YouTube
YouTube
Disney+
Disney+
Max
Max
Apple TV+
Apple TV+
Paramount+
Paramount+
Peacock
Peacock
Amazon Prime Video
Amazon Prime Video
Twitch
Twitch
SiriusXM
SiriusXM
Tidal
Tidal
Deezer
Deezer
iHeartMedia
iHeartMedia
Audible
Audible
Crunchyroll
Crunchyroll
DAZN
DAZN
FuboTV
FuboTV
Philo
Philo
Parrot Analytics
Parrot Analytics

Difficulty Distribution

Easy

25

20% of problems

Medium

48

38% of problems

Hard

44

35% of problems

Expert

8

6% of problems

What You'll Practice

Watch time analysis
Content completion rates
Subscription retention
Recommendation signal metrics
Content ROI analysis
User engagement metrics
Binge-watching patterns
Audio & video performance

Topics Covered

21 topics
SQL· 9
aggregationbasic queries filteringcleaning transformdate timejoinsscenario sqlset operationssubqueries cteswindow functions
Python· 12
eda statisticsfeature engineeringpandas aggregationpandas applypandas basicspandas cleaningpandas datetimepandas filteringpandas mergingpandas reshapingpandas scenariopandas window

All Problems125 total

Open in editor
01
Active Action MoviesFree
SQLEasy
02
Long Completed UHD/4K SessionsFree
SQLMedium
03
Active Subscribers With Recent UHD/4K ActivityFree
SQLHard
04
Latest Playback Session per UserFree
SQLMedium
05
Latest Active Subscription per UserFree
SQLMedium
06
Highest-Priced Active Plan per RegionFree
SQLMedium
07
Top 3 Longest Sessions per User — Last 120 DaysFree
SQLHard
08
Region Revenue Ranking — Last 90 DaysFree
SQLHard
09
Streaming Payments by Most Recent TimeFree
SQLEasy
10
Sessions Started in the Device’s Registration MonthFree
SQLMedium
11
Normalize Streaming User LocalesFree
SQLMedium
12
Completed Playback Sessions (Normalized)Free
SQLMedium
13
Active Originals in a Specific GenreFree
SQLEasy
14
Active Users With Single DeviceFree
SQLMedium
15
Latest qualifying session per device for active subscribersFree
SQLHard
16
Filter Users by CountryFree
PYTHONEasy
17
Filter North American Active UsersFree
PYTHONHard
18
Filter US SubscribersFree
PYTHONEasy
19
Filter Users from Multiple CountriesFree
PYTHONMedium
20
Count Users by CountryFree
PYTHONEasy
21
Unique Countries by Account StatusFree
PYTHONMedium
22
Melt Plan PricingFree
PYTHONEasy
23
Pivot Payments by Method and StatusFree
PYTHONMedium
24
Pivot Playback by Device and QualityFree
PYTHONHard
25
Rolling Average Watch TimeFree
PYTHONEasy
26
Calculate Payment ChangeFree
PYTHONMedium
27
Rolling Average per UserFree
PYTHONHard
28
Filter Subscriptions by Date RangeFree
PYTHONEasy
29
Aggregate Payments by MonthFree
PYTHONMedium
30
Weekend vs Weekday Playback AnalysisFree
PYTHONMedium
31
Replace Status CodesFree
PYTHONMedium
32
Clean Subscription Analytics DataFree
PYTHONHard
33
Map Status to LabelsFree
PYTHONEasy
34
Session Quality AnalysisFree
PYTHONHard
35
Viewer Activity SummaryFree
PYTHONMedium
36
Active Users in Target CountriesPro
SQLEasy
37
Original Movies in CatalogPro
SQLEasy
38
Failed Subscription PaymentsPro
SQLEasy
39
High-Rated Titles with ReviewsPro
SQLMedium
40
Long Completed Playback SessionsPro
SQLMedium
41
Active Subscriptions with Plan DetailsPro
SQLEasy
42
Completed Playback with User and TitlePro
SQLMedium
43
Captured Payments with User and PlanPro
SQLMedium
44
High Ratings with User and TitlePro
SQLMedium
45
Titles Never WatchedPro
SQLHard
46
Watchers Who Never RatedPro
SQLHard
47
Playback Session Count by StatusPro
SQLEasy
48
Average Rating by GenrePro
SQLMedium
49
Total Watch Time Per UserPro
SQLMedium
50
Users With Multiple Devices Per TypePro
SQLMedium
51
Top Five Most-Watched TitlesPro
SQLHard
52
Revenue by Plan Type With Refund RatePro
SQLHard
53
Rank Titles by Total Watch TimePro
SQLMedium
54
Number Each User's Playback SessionsPro
SQLMedium
55
Running Total of Daily PaymentsPro
SQLHard
56
Top Title per Genre by RatingPro
SQLHard
57
Monthly Watch Hours Moving AveragePro
SQLHard
58
Payment Amount Change from PreviousPro
SQLHard
59
User Watch Time Quartile AnalysisPro
SQLHard
60
Titles Rated Above AveragePro
SQLMedium
61
Users Who Watched and Rated the Same TitlePro
SQLHard
62
Most Recent Subscription per UserPro
SQLHard
63
Watchlisted but Never WatchedPro
SQLHard
64
Content Engagement ScorecardPro
SQLExpert
65
Users Signed Up in 2024Pro
SQLEasy
66
Monthly Playback Session VolumePro
SQLMedium
67
Average Subscription Duration by StatusPro
SQLMedium
68
User Tenure vs Watch ActivityPro
SQLHard
69
Playback Quality Tier LabelsPro
SQLEasy
70
User Engagement Tier ClassificationPro
SQLMedium
71
Content Maturity Risk ReportPro
SQLHard
72
Titles Watched or RatedPro
SQLMedium
73
Former Subscribers with PaymentsPro
SQLMedium
74
Content Performance ScorecardPro
SQLHard
75
User Churn Risk AssessmentPro
SQLHard
76
Device Platform AnalyticsPro
SQLHard
77
Payment Health DashboardPro
SQLHard
78
Subscriber Lifetime Value ReportPro
SQLExpert
79
Content Catalog Health AnalysisPro
SQLExpert
80
Streaming Platform User DashboardPro
SQLExpert
81
Active User ProfilesPro
PYTHONEasy
82
Content Type CountsPro
PYTHONEasy
83
Device Type SummaryPro
PYTHONMedium
84
Plan Quality OverviewPro
PYTHONMedium
85
Active Auto-Renewing SubscriptionsPro
PYTHONEasy
86
Recent Active Original TitlesPro
PYTHONEasy
87
Long Playback SessionsPro
PYTHONMedium
88
Failed Payments Since July 2024Pro
PYTHONMedium
89
Sessions by StatusPro
PYTHONEasy
90
Total Watch Time by DevicePro
PYTHONMedium
91
Average Rating by GenrePro
PYTHONMedium
92
Payment Stats by ProcessorPro
PYTHONHard
93
User Engagement SummaryPro
PYTHONHard
94
Subscriptions with Plan NamesPro
PYTHONEasy
95
Average Rating per TitlePro
PYTHONMedium
96
Titles Not in Any WatchlistPro
PYTHONMedium
97
Enriched Movie Playback LogPro
PYTHONHard
98
Session Full HierarchyPro
PYTHONHard
99
Rank Users by Watch TimePro
PYTHONMedium
100
Cumulative Payments Per UserPro
PYTHONMedium
101
Seven-Day Rolling Watch TimePro
PYTHONHard
102
Daily Session ChangePro
PYTHONHard
103
Subscription Start Month and YearPro
PYTHONEasy
104
Subscription Duration in DaysPro
PYTHONMedium
105
Monthly Session Volume and Watch HoursPro
PYTHONHard
106
Fill Missing Subscription End DatesPro
PYTHONEasy
107
Normalize Payment Amounts to USDPro
PYTHONMedium
108
Standardize Device OS into Platform CategoriesPro
PYTHONHard
109
Pivot Sessions by QualityPro
PYTHONMedium
110
Pivot Rating Distribution by GenrePro
PYTHONHard
111
Classify Titles by MaturityPro
PYTHONMedium
112
Session Engagement ScorePro
PYTHONHard
113
Compute Session Completion RatePro
PYTHONMedium
114
Watch-Time Quartile BucketingPro
PYTHONHard
115
Time-Based Session FeaturesPro
PYTHONHard
116
User Feature MatrixPro
PYTHONExpert
117
Title Engagement FeaturesPro
PYTHONHard
118
Payment Amount Descriptive StatsPro
PYTHONMedium
119
Watch Time vs Rating CorrelationPro
PYTHONHard
120
Anomalous Watch Time DetectionPro
PYTHONHard
121
Content Performance ScorecardPro
PYTHONHard
122
Subscriber Churn Risk ReportPro
PYTHONHard
123
Content Recommendation PipelinePro
PYTHONExpert
124
Subscriber Health ClassificationPro
PYTHONExpert
125
Cross-Device Viewing AnalysisPro
PYTHONExpert

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