Edge Case Handling and Debugging Questions

Covers the systematic identification, analysis, and mitigation of edge cases and failures across code and user flows. Topics include methodically enumerating boundary conditions and unusual inputs such as empty inputs, single elements, large inputs, duplicates, negative numbers, integer overflow, circular structures, and null values; writing defensive code with input validation, null checks, and guard clauses; designing and handling error states including network timeouts, permission denials, and form validation failures; creating clear actionable error messages and informative empty states for users; methodical debugging techniques to trace logic errors, reproduce failing cases, and fix root causes; and testing strategies to validate robustness before submission. Also includes communicating edge case reasoning to interviewers and demonstrating a structured troubleshooting process.

MediumTechnical

35 practiced

Design a robust approach for handling late-arriving transactions and duplicate events in a nightly ETL that builds a daily metrics table used by dashboards. Include detection, deduplication, reprocessing windows, idempotent writes, and how dashboards should expose data freshness to end-users.

MediumSystem Design

40 practiced

Design an observability and alerting strategy for critical BI datasets: list the metrics to collect (freshness, row counts, null ratios, schema drift), thresholds or anomaly detection methods, alert sinks (Slack, email, incident system), and runbook steps for each alert type.

MediumTechnical

36 practiced

Provide three concrete examples of informative empty states for dashboards where a KPI returns no data (e.g., conversion rate undefined, no transactions in period). For each example include: suggested messaging, visual cues or placeholders, troubleshooting steps for users, and backend metrics to collect to detect the empty state.

HardTechnical

41 practiced

Your revenue is stored in cents as INTEGER in an upstream system. Over years of growth, this could overflow. Propose a testing and monitoring approach to detect and prevent integer overflow across ETL and analytics: include synthetic tests, schema checks, runtime guards, and alerting.

MediumTechnical

46 practiced

You run an A/B test and see a statistically significant increase in page views but no change in conversion rate. Suppose instrumentation duplication is suspected and may only affect variant B. Describe how you'd detect, diagnose, and correct this in your analysis pipeline and how you'd report corrections to stakeholders.

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