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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.

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.
MediumBehavioral
0 practiced
Tell me about a time you debugged a critical ML model failure under time pressure. Use the STAR format: Situation, Task, Action, Result. Focus on how you reproduced the issue, isolated the root cause, verified the fix, and communicated during the incident.
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
Design a Python helper function that uses git CLI to perform a bisect over a list of recent commits to find the commit that introduced a failing test. Provide the function interface, describe how it runs tests, handles flaky tests, and what assumptions you make about the environment.
HardTechnical
0 practiced
You have to decide whether to rollback to a previous checkpoint or attempt to patch a training script that introduced a subtle regression. Formulate a decision framework that quantifies risk, rollback cost, expected benefit, test coverage, and stakeholder impact. Show how you would present the recommendation to a PM within 10 minutes.

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