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Debugging and Recovery Under Pressure Questions

Covers systematic approaches to finding and fixing bugs during time pressured situations such as interviews, plus techniques for verifying correctness and recovering gracefully when an initial approach fails. Topics include reproducing the failure, isolating the minimal failing case, stepping through logic mentally or with print statements, and using binary search or divide and conquer to narrow the fault. Emphasize careful assumption checking, invariant validation, and common error classes such as off by one, null or boundary conditions, integer overflow, and index errors. Verification practices include creating and running representative test cases: normal inputs, edge cases, empty and single element inputs, duplicates, boundary values, large inputs, and randomized or stress tests when feasible. Time management and recovery strategies are covered: prioritize the smallest fix that restores correctness, preserve working state, revert to a simpler correct solution if necessary, communicate reasoning aloud, avoid blind or random edits, and demonstrate calm, structured troubleshooting rather than panic. The goal is to show rigorous debugging methodology, build trust in the final solution through targeted verification, and display resilience and recovery strategy under interview pressure.

EasyTechnical
0 practiced
Before making a risky code change to a training pipeline under interview pressure, what three fast ways would you preserve the current working state (code, data, model) and why does each matter for rollback and forensic analysis?
MediumTechnical
0 practiced
You need to design a small test harness for transformer inference that allows rapid A/B testing between tokenizer variants and dataset subsets. List the components, minimal interfaces, and the set of smoke and regression tests you'd include to validate that tokenization, batching, and model input alignment are correct under time pressure.
HardTechnical
0 practiced
Explain common causes of integer overflow and floating-point instabilities in deep learning (examples: softmax on large logits, catastrophic cancellation, mixed-precision accumulation). For each cause, provide detection strategies, an immediate mitigation for a live incident, and a long-term fix.
EasyTechnical
0 practiced
Gradients in a small training run suddenly become NaN. Under interview time pressure, list a methodical checklist of immediate sanity checks and quick mitigations you would perform to find and recover from NaN gradients in a deep learning model.
MediumTechnical
0 practiced
A GPU OOM occurs in CI but not locally. You have 30 minutes to reproduce and mitigate. Provide a prioritized, systematic plan to reproduce the OOM, find the root cause, and apply a quick mitigation so CI can continue while you investigate further.

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