The interviewer provides pre-written buggy code involving numpy arrays, tensor operations, and distributed computing concepts.
The interviewer provides pre-written buggy code involving numpy arrays, tensor operations, and distributed computing concepts. The candidate must read and understand the code, identify the bugs, and rewrite the problematic sections. Known bug categories observed include: (1) Matrix.zeros aliasing issues, (2) incorrect or missing axis parameter handling in to_ndarray (e.g., missing axis=1) and from_ndarray usage, and (3) remainder truncation errors in distributed/tensor operations.
Why does this aliasing occur and how would you fix it?
What is the effect of omitting axis=1 here, and what is the correct behavior?
How would you handle the case where the total workload does not evenly divide across workers?
| Approach | Notes |
|---|---|
| Test-case driven debugging | Running mental (or written) test cases against the buggy code can help surface incorrect outputs, but may be slow in an interview setting and may miss subtle aliasing or truncation bugs without careful edge-case selection. |
Common mistakes: Failing to identify the bugs at all — the candidate who reported failing this round explicitly said they could not find the bugs (Matrix.zeros aliasing, missing axis=1 in to_ndarray/from_ndarray, remainder truncation in distributed ops).; Going into the round under-prepared for numpy/tensor/distributed debugging specifics; the failing candidate noted the round felt 'hardcore' and they had not expected this format.
Interviewer hints: The interviewer (described as a 'American big sister') pre-writes the buggy code herself and presents it to the candidate; the task is to read, identify bugs, and rewrite the broken sections — not to write a solution from scratch.
What passers do: The second report describes the format: the interviewer writes a buggy version of the code directly, then asks the candidate to find bugs and rewrite the problematic sections. Candidates who engage with the pre-written code systematically rather than writing from scratch appear to fit what the round expects.