Netflix-Specific Data Analysis Scenarios Questions
Netflix-specific data analysis scenarios covering streaming metrics, user engagement and retention analysis, content consumption patterns, evaluation of recommendation systems, A/B test design and analysis, cohort analysis, data visualization, and storytelling with data in the streaming domain.
HardTechnical
0 practiced
Schema: events(user_id BIGINT, content_id BIGINT, event_time TIMESTAMP, event_type STRING). For each content_id compute the retention decay curve: fraction of users who return to watch the same content on day 1..180 after their first watch. Provide an efficient PySpark implementation or SQL approach suitable for billions of rows, and discuss partitioning, join strategies, avoiding wide shuffles, and approximation techniques (sampling) for scale.
MediumTechnical
0 practiced
You ran a 2-week experiment: control N=1000 users (mean daily watch-time = 40 min, sd=20), treatment N=1000 users (mean = 42.5 min, sd=22). Perform a two-sample t-test for difference in means and describe how you would compute a bootstrap confidence interval for the mean difference. State assumptions for both approaches and interpret results for a business stakeholder.
MediumTechnical
0 practiced
Write a SQL query (use your preferred dialect) that computes for each signup_week cohort the 30-day rolling retention per day (day 0..30) using window functions. Input tables: users(user_id, signup_date), events(user_id, event_date). Output: cohort_week, day_after_signup, retention_rate. Explain performance considerations for large datasets.
HardTechnical
0 practiced
Design an analysis and mitigation plan to ensure underrepresented creators receive fair exposure in Netflix recommendations. Define measurable exposure-fairness metrics (e.g., exposure share, disparity ratio), describe offline tests, propose mitigation methods (re-ranking with constraints, multi-objective optimization), and explain business trade-offs and monitoring/rollback strategies.
MediumTechnical
0 practiced
Design an interactive dashboard for the content team to support renewal decisions. Describe which KPIs (e.g., total minutes, unique viewers, retention of viewers, acquisition source), visualizations (trend lines, cohort retention heatmap, demographic breakdowns), filters/drill-downs, and confidence/uncertainty indicators you would include. Explain how early indicators for new shows should be surfaced.
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