Kubernetes metrics collection
Design metrics collection for a highly elastic environment: a Kubernetes platform where 30,000 pods churn constantly (median pod lifetime 20 minutes), spread across 12 clusters in 4 regions. You need every pod's metrics scraped within 30s of it appearing, no metric gaps when pods die, and a single global query view across all regions. Decide push vs pull, how targets are discovered, and how you make collection highly available without double-counting.
What a strong answer looks like
Clarify scale and constraints first. Propose a clean component breakdown, then go deep on the hard parts (data model, bottlenecks, consistency, failure modes) and name the trade-offs you are making.
Clarify4:00 left
Estimate4:00 planned
Design11:30 planned
Deep dive9:30 planned
Failure6:00 planned
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.