Debug and harden trial-assignment Python code

Q: Debug and harden trial-assignment Python code

This is a Data Manipulation (SQL/Python) interview question from OpenAI for Data Scientist roles. View the full question and solution on PracHub.

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Question

You are given the following simplified Python snippet used to assign users and trigger a 1‑month free trial:

""" import random, datetime, requests

def maybe_start_trial(user_id): if random.random() < 0.5: variant = 'trial' else: variant = 'control' now = datetime.datetime.now() exposed_at = now log_exposure(user_id, exposed_at, variant) if variant == 'trial': r = requests.post('https://billing/start_trial', json={'user_id': user_id}) if r.status_code == 200: log_trial(user_id, now) """

Identify at least 8 distinct correctness or reliability issues that could corrupt the experiment or data (think assignment stickiness, bias, timezones, idempotency, retries, race conditions, privacy, and observability). For each, explain the failure mode and its impact on metrics. Then propose concrete fixes (e.g., user‑hash bucketing with stable salts, UTC timestamps, exactly‑once exposure logging, outbox/queue, idempotency keys, transactional writes, circuit breakers, exponential backoff, and eligibility checks). Finally, sketch a hardened version (high‑level or pseudocode) that is production‑ready and experiment‑safe.

Debug and harden trial-assignment Python code

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