This wrecked me a little at first because I came in expecting a stats question and got handed code to audit.
First, clarify the experiment's goals and constraints, then systematically review the code for issues in randomization, data logging, API reliability, and error handling. For each issue, explain the potential impact on experiment validity and propose a concrete fix, prioritizing those that could corrupt data or bias results.
Pro tip: Emphasize that even minor reliability issues can compound into significant data corruption, and always consider idempotency and observability in fixes to prevent silent failures.
Ask about the expected scale, latency requirements, and tolerance for data loss to tailor your analysis. This shows you understand the business context before diving into code.
Check if the assignment is truly random, deterministic per user, and logged correctly. Look for issues like using non-cryptographic randomness, lack of user ID hashing, or inconsistent assignment.
Examine how the API call is made: error handling, retries, timeouts, and idempotency. Consider what happens if the call fails or succeeds but the response is lost.
Ensure that assignment and billing outcomes are logged atomically and consistently. Look for race conditions, missing logs, or logs that don't match actual actions.
For each issue, suggest a concrete fix and mention any trade-offs (e.g., added latency, complexity). Prioritize fixes that prevent data corruption or bias.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.