Model lazy load cold start
During a routine rolling deploy of your translation inference service, the error rate spikes to 8% for ~6 minutes each time a batch of pods cycles, then recovers. Symptom repeats on every rollout. Dashboards: newly started pods report Ready and receive traffic immediately, but their first ~90 seconds show very high latency and timeouts; the model artifact is a multi-GB file pulled from object storage at startup, and the first inference triggers lazy GPU kernel/weight loading. p99 on warm pods is fine. Triage and fix.
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.