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
Propose an algorithm and feature set to detect bot or automated streaming behavior contaminating analytics (e.g., suspiciously high concurrent streams, non-human inter-play intervals). Explain how you'd score users, what thresholds to use, how to validate detections, and how you'd remove or label such traffic in downstream reports.
HardTechnical
0 practiced
Case study: Netflix is considering acquiring niche content to capture long-tail audiences. You are asked to analyze long-tail content consumption and recommend an acquisition strategy. Describe the data you would pull, metrics to compute (e.g., watch minutes per title, cost per watch, discovery rate), how you'd segment titles, and the decision rule for recommending content acquisition.
HardSystem Design
0 practiced
Design an architecture to compute and serve 'top trending titles' updated every minute globally across regions. Requirements: handle ~100k events/sec peak, produce top-100 per region, support eventual consistency, and low query latency for dashboards and API. Discuss stream processing approach, approximate vs exact algorithms, caching, and failure scenarios.
MediumTechnical
0 practiced
Given per-play data of play_seconds by user, propose an approach (SQL or Python) to detect outlier viewing times per user and per title (e.g., bots, extreme binge sessions). Describe feature engineering, thresholds or model choices, and how to surface suspected anomalies to data quality teams.
EasyTechnical
0 practiced
Write an ANSI SQL query to compute 'watch minutes per active user' for the last 30 days. Use tables: users(user_id) and play_events(user_id, play_seconds, occurred_at). Return date, active_users, total_watch_minutes, watch_minutes_per_active_user. Clarify assumptions about active user definition.
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