Batch job rate limit from parallelization
Your fintech app syncs bank-account transactions through an aggregator vendor (Plaid-style). At 08:00 a nightly batch sync that pulls fresh data for all users starts failing partway through. Dashboards: the vendor returns HTTP 429 'rate limit exceeded' after the first few thousand accounts; the rest of the batch errors; your job has no concurrency throttle and fires requests as fast as it can. Recent context: your user base grew 40% this quarter, and someone parallelized the sync job last month to 'make it finish faster.' How do you triage and mitigate?
What a strong answer looks like
Stop the bleeding first (mitigate), then form hypotheses from real signals. Separate root cause from symptom, communicate status as you go, and close with what prevents a repeat.
0:00 of about 30 min
Which questions mattered is sealed until you submit. Telling you now would just be handing over the edge cases.
Run or narrate your approach, then ask the coach.