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.
MediumTechnical
0 practiced
Describe a lightweight data privacy checklist for product analytics experiments at Netflix that captures PII minimization, aggregation thresholds, and differential privacy considerations before exposing experiment dashboards to non-data teams. What concrete steps would you include in the release process?
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.
EasyTechnical
0 practiced
Given a streaming_sessions table schema: streaming_sessions(session_id, user_id, title_id, watch_seconds, started_at, ended_at), write an SQL query to compute the median watch_seconds per title for the last 30 days excluding obvious outliers. Define an outlier rule you would use and show how you apply it in the query. Describe briefly how your approach scales on large datasets.
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.
HardSystem Design
0 practiced
You own a model that predicts 7-day retention. Design the operationalization plan: retraining cadence, feature lineage and reproducibility, canary deployment plan (including traffic split and monitoring metrics), rollback criteria, and SLOs for model performance and data freshness.
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