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

Design a fuzz-testing harness specifically for tokenizers and prompt handling in NLP systems. Include Unicode corner cases, extremely long inputs, invalid byte sequences, nested special tokens, adversarial sequences that might trigger resource exhaustion or security flaws, and a throttled mode suitable for CI. Describe how to triage, reproduce, and prioritize found issues.

MediumTechnical

0 practiced

Your NLP model has a fixed context window of 2048 tokens. Describe concrete strategies to handle user inputs longer than this: truncation, sliding window with overlap, hierarchical encoding, retrieval-augmented generation, and on-the-fly summarization. For each strategy list edge cases and explicit tests you would write to validate correctness and user experience.

EasyTechnical

0 practiced

Explain numeric overflow versus underflow and describe how each can manifest during training and inference (e.g., loss exploding to Inf, gradients vanishing to zero). For each issue provide practical mitigations such as gradient clipping, mixed precision techniques, dynamic loss scaling, normalization layers, and instrumentation you would add to detect early signs.

MediumTechnical

0 practiced

Implement deduplicate_results(results: List[Tuple[str, float]]) -> List[Tuple[str, float]] in Python for a similarity search service where results may include repeated ids. Requirements: preserve the highest score per id, stable ordering when scores tie, remove near-duplicates according to a configurable similarity threshold, and ensure efficiency up to 10k items. Explain complexity and memory usage.

HardTechnical

0 practiced

Deep technical: Discuss quantization edge cases in ML models. Explain how representational error, saturation, and rounding can propagate through layers causing misclassification, differences between per-channel and per-tensor quantization, and outlier handling strategies during calibration. Propose experiments to detect quantization-induced failures and how to mitigate them.

Unlock Full Question Bank

Get access to hundreds of Edge Case Handling and Debugging interview questions and detailed answers.

Join thousands of developers preparing for their dream job.