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.

HardTechnical

0 practiced

You compute recursive graph features (e.g., influence scores) where node relationships may contain cycles and self-references. Explain how to detect and avoid infinite recursion and prevent excessive compute: iterative methods, memoization, convergence criteria, maximum depth, and parallelizable approximations. Propose test graphs (acyclic, simple cycles, dense cycles) to validate correctness and performance.

MediumTechnical

0 practiced

A training job produces non-deterministic results: two runs with the same random seed yield different validation metrics. Describe a systematic debugging checklist to identify sources of nondeterminism (data loader shuffling, multi-threading, cuDNN nondeterminism, library versions, asynchronous operators), how to make training deterministic, and what tests you'd add to CI to detect regressions in determinism.

EasyTechnical

0 practiced

You are training a classifier with significant class imbalance and rare edge-case examples. Describe practical strategies to detect and mitigate rare-class edge cases: dataset analysis approaches to quantify imbalance, resampling (oversampling, SMOTE), class-weighted losses, threshold tuning, targeted data collection and data augmentation. For each strategy discuss the trade-offs and how you'd validate that the model improved on the rare cases without harming overall performance.

MediumTechnical

0 practiced

Write pytest-style unit tests to validate model serialization/deserialization across minor version changes. Tests should verify: model loads without exceptions, predictions before and after save/load are within numeric tolerance when using a fixed seed, and that corrupted or partial files raise clear exceptions. Describe how you would simulate a corrupted checkpoint and assert graceful failure.

MediumSystem Design

0 practiced

Design the logging and observability stack for a production ML model. What metrics would you collect (latency p50/p95/p99, input feature distributions, prediction distribution, model confidence, error rates)? What logs/traces would you keep? Define SLIs/SLOs and alert rules, sampling and retention strategy to balance cost vs debuggability, and three example dashboards you would build for on-call engineers and ML owners.

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