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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
Production inference intermittently returns NaNs under heavy load across several containers. You're remote on a paging call. Explain an incident triage: what logs, traces, and core dumps you'd collect from worker containers and GPUs, how you'd determine whether it's a serving or model-internal issue, and a short recovery plan that minimizes user impact (e.g., scale down, traffic routing, rollback).
EasyTechnical
0 practiced
During an interview coding task you get an IndexError when accessing model outputs by predicted indices. Describe the mental debugging steps and a minimal unit test you would write immediately to reproduce the issue and confirm the fix. Mention what prints/log lines you'd add and why.
EasyTechnical
0 practiced
You are given 10 minutes in an interview: a recently trained classifier is not converging on a small reproducible toy dataset. Describe, step-by-step, your debugging approach under time pressure. Include how you would (a) reproduce the failure, (b) isolate a minimal failing case, (c) prioritize checks and fixes, and (d) communicate findings aloud to the interviewer while preserving working state.
EasyTechnical
0 practiced
Implement a small Python function that performs a numerical gradient check for a scalar function f(x) where x is a 1-D numpy array. Use centered differences and compare the analytic gradient (provided as another function) to the numerical gradient; return the maximum absolute difference. Include a test case where f(x) = sum(x**3) and analytic gradient 3*x**2.
EasyBehavioral
0 practiced
Tell me about a time when you discovered and fixed a production ML bug under tight deadlines. Use the STAR format: situation, task, action, result — emphasize how you reproduced the issue, prioritized checks, communicated stakeholders, and what you changed to avoid recurrence.

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