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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.

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.
HardTechnical
0 practiced
Problem solving: A batched inference endpoint produces inconsistent outputs between CPU and GPU when under high concurrency. Provide a structured debugging plan: how to reproduce with minimal batch, check for non-deterministic ops or race conditions, verify numerical precision differences, inspect batching code for ordering issues, and propose tests to catch the issue in CI.
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.
EasyTechnical
0 practiced
Implement topk_safe(scores: List[float], k: int) -> List[int] in Python that returns indices of top-k scores robustly. Requirements: handle NaN/Inf (place them at end or reject), k <= 0, k > len(scores), ties should be deterministically broken (stable order), and duplicate items allowed. Document complexity and edge-case behavior.
MediumTechnical
0 practiced
Scenario: A production generative model sometimes outputs personal identifiable information (PII). Outline immediate mitigations (filtering, rate limiting), steps for root-cause investigation, automated tests to prevent regression, monitoring to detect future leaks, and a communication plan to stakeholders and affected users. Include short-term and long-term fixes.

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