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.
HardSystem Design
51 practiced
Given a streaming platform with Kafka (200 partitions) and peak throughput of 1M events/sec, design a deduplication and aggregation approach to compute near-real-time metrics with at-most-once vs exactly-once semantics. Discuss watermarking, state backends (RocksDB, Flink state), checkpointing, latency targets, and trade-offs between correctness and resource cost.
HardSystem Design
53 practiced
Architect a monitoring and alerting system for model drift and business-impacting degradation where the business KPI is revenue per user (RPU). Describe pipelines to collect delayed ground truth, statistical drift detectors for features and predictions, linking model metric drift to RPU impact, thresholds for alerts, and automated vs manual remediation workflows.
EasyTechnical
54 practiced
Design an executive-level weekly dashboard focused on product growth and health. List panels and metrics (trend, cohort, acquisition, retention, revenue, anomalies), suggested drill-downs for PMs and data scientists, and how you'd surface uncertainty or statistical significance for week-to-week changes to avoid overreaction.
HardTechnical
46 practiced
Devise a method to backfill missing historical labels for model evaluation when labels are delayed or partially observed. Discuss probabilistic imputation approaches, EM-like algorithms, importance weighting, and how to quantify and communicate bias introduced by backfill when comparing models.
EasyTechnical
47 practiced
Your product manager asks: 'Increase 7-day retention by 5% within the next quarter' for a mobile social app. List the exact data points you would request (events and attributes), derived metrics you'd compute, critical assumptions to state, instrumentation changes required, and a minimal analysis plan to measure progress weekly.
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