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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.

EasyTechnical
48 practiced
You aggregate billions of rows computing counts and sums. Describe edge cases that can cause integer overflow or precision loss (32-bit overflow, float accumulation error, large SUM beyond type range). What defensive checks, data types (bigint/decimal), and monitoring would you implement? How would you write tests to catch overflow before production?
MediumTechnical
39 practiced
You have a transformation that normalizes international postal addresses. Describe a testing strategy combining unit tests, integration tests (with external address APIs mocked), property-based tests to assert invariants (e.g., zip format, required fields), and curated sample datasets that include PO boxes, international variations, and broken inputs.
EasyTechnical
42 practiced
How do you document known edge cases, validation rules, and assumptions for data pipelines so downstream consumers understand expected behavior? Describe repository organization, schema docs, sample bad-good datasets, automated tests that keep docs current, and how to expose this in a data catalog.
MediumTechnical
44 practiced
Explain how you would implement fuzz testing or property-based tests for an ingestion/transformation pipeline to discover edge inputs such as malformed JSON, extreme numeric values, nested arrays, and strange character encodings. Include tooling choices, strategies for realistic input generation, and CI integration.
MediumTechnical
64 practiced
A Spark job is OOM'ing during a groupBy because of data skew. Describe how to detect skew using Spark UI (task times, shuffle sizes, skewed partitions), and mitigation techniques: salting keys, map-side pre-aggregation, custom partitioners, broadcast joins, and sampling to validate fixes.

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