GPU autoscaler CPU metric mismatch
Traffic to your GPU-backed LLM inference service rises 3x for a product launch. The HPA is configured to scale on CPU utilization, but CPU stays at ~35% while GPUs are pinned at 100% and p99 latency climbs and requests start timing out — yet no new pods are added. When you manually bump replicas, the new GPU pods sit Pending for several minutes before serving. Dashboards: GPU utilization 100%, GPU memory high, CPU low, cluster GPU node count flat, and a pending-pod count rising. Triage and stabilize.
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 35 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.