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
You need to build a churn prediction model to flag users likely to cancel subscription within 30 days. Describe feature groups you would engineer from streaming logs, model choices (algorithms), evaluation metrics appropriate for business action, and how you would validate and deploy the model to production for near-real-time scoring.
MediumTechnical
0 practiced
You want to detect whether a UI change caused an uplift in engagement but only a subset of users received the change non-randomly in historical logs. Sketch a pragmatic approach to estimate causal effect from observational logs including model-based adjustments and a plan to validate findings with a prospective randomized test.
HardSystem Design
0 practiced
Design a low-latency model serving architecture to provide personalized ranking per user at playback start with latency under 50ms at 100k QPS. Include feature store design (online vs offline), model size considerations, caching strategies (user and item), candidate retrieval approach (ANN/embedding), and fallback behavior when features are missing.
HardTechnical
0 practiced
For a complex deep recommender model serving millions of users and items, design an approach to provide per-recommendation explanations at low latency that are privacy preserving. Consider precomputation, surrogate models, caching, and strategies to avoid leaking user private attributes in the explanation.
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.
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