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.
MediumSystem Design
0 practiced
Design an end-to-end streaming pipeline to collect client-side events (play/pause/seek) from devices to produce per-title hourly analytics for dashboards (e.g., hourly watch-time, average bitrate). Include ingestion technology, schema registry considerations, aggregation layer, storage for analytics, and how to support backfills and late-arriving events.
MediumTechnical
0 practiced
Design an A/B experiment to evaluate a new recommendation algorithm that will be rolled out in US and non-US regions. Describe: randomization unit, stratification variables, assignment ratio, primary and guardrail metrics, sample size considerations across regions, and monitoring/rollback rules you would implement during the experiment.
EasyTechnical
0 practiced
At Netflix, teams track many streaming metrics. List and define five essential streaming metrics you would report to product stakeholders (for example: MAU, DAU, average viewing time per user, session starts, completion rate). For each metric provide: 1) exact formula using these example tables: users(user_id, signup_date) and events(user_id, event_type, event_timestamp, title_id, watch_seconds); 2) primary data source and fields required; and 3) one common pitfall in production. Finally, explain how you would compute these metrics daily for global and regional slices.
MediumTechnical
0 practiced
You observe experiment output: control conversion = 2.50% (n=50,000), treatment = 2.80% (n=50,000). Compute the p-value and 95% confidence interval for the difference using the normal approximation for two proportions. Show calculations and state whether the result is statistically significant at alpha=0.05.
MediumTechnical
0 practiced
Explain how inverse propensity scoring (IPS) can be used to evaluate a new recommendation policy offline using historical logged bandit data. State the key assumptions required, how you would clip weights to reduce variance, and one method to further reduce estimator variance.
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