Data Analysis and Requirements Translation Questions
Focuses on translating ambiguous business questions into concrete data analysis plans. Candidates should identify the data points required, define metrics and key performance indicators, state assumptions to validate, design the analysis steps and queries, and explain how analysis results map back to business decisions. This includes data quality considerations, required instrumentation, and how analytical findings influence product requirements or architectural choices.
MediumSystem Design
0 practiced
Design a daily retraining data pipeline for a recommendation model with 50M users and 100M events/day. Requirements: nightly retrain, reproducibility of training runs, feature store with online/offline features, and cost-conscious compute. Sketch components (ingest, ETL, feature computation, training, validation, deploy), data storage choices, and how you would instrument for data lineage and reproducibility.
EasyBehavioral
0 practiced
How would you explain complex analysis results that include uncertainty to non-technical product stakeholders and obtain alignment on next steps? Provide a step-by-step approach for preparing the communication, visualizations to use, and techniques to translate statistical uncertainty into actionable decisions.
MediumTechnical
0 practiced
Estimate the business impact in dollars of improving average session length by 5% for a subscription video service. State the data required (ARPU, churn elasticity, ad revenue per minute), the modeling approach to translate session length changes into revenue (direct vs indirect effects), and how to compute confidence intervals given uncertainty in elasticities.
HardTechnical
0 practiced
Given a partitioned events table in BigQuery with schema events(user_id STRING, occurred_at TIMESTAMP, event_name STRING), write an optimized SQL query to compute per-user average inter-event time while ignoring gaps greater than 30 minutes (session boundary). Explain how partitioning, window functions, and query patterns reduce cost on large datasets and estimate runtime considerations.
MediumTechnical
0 practiced
Given observational logs with treatment flag and outcome, provide SQL or Python steps to estimate the uplift (incremental effect) of a marketing campaign using propensity score matching. Include schema assumptions, how to compute propensity scores, creating matched controls, and how to compute and report confidence intervals and key assumptions for validity.
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