Meta Site Reliability Engineer Interview Questions

I would first focus on stabilizing the user impact, even before the ownership question is fully resolved. Then I would identify the most likely responsible team, bring in the right people, and keep a visible incident timeline so accountability is clear. The reason I choose this approach is that incidents should not stall because of ownership ambiguity. I would also think about how to improve ownership metadata, escalation paths, and runbooks afterward. If the interviewer wants more detail, I would describe how I would prevent that ambiguity from recurring.

I would start with a hypothesis about what failure mode I want to test and define the blast radius very carefully. Then I would run controlled experiments in non-production or limited-production environments with strong rollback and observability. The reason I choose this approach is that chaos engineering should teach us about resilience without causing unacceptable harm. I would also think about game days, stakeholder approval, and how to measure the outcome of each experiment. If the interviewer wants more detail, I would explain how I would turn findings into engineering changes.

I would choose the path that restores service the fastest with the least risk. If the bug is isolated and easy to patch safely, a hotfix may be best; if the new version is broadly unstable, rollback is usually safer. The reason I choose this decision rule is that during an outage, reducing user impact matters more than elegance. I would also think about whether the rollback is actually safe, especially if the schema or dependencies changed. If the interviewer wants more detail, I would explain how I would communicate that decision to stakeholders.

I would combine autoscaling, queueing, caching, and load shedding so the system can absorb sudden demand without collapsing. Then I would validate that the system still behaves predictably under overload instead of failing all at once. The reason I choose this approach is that traffic spikes are easier to manage when the service can degrade gracefully. I would also think about rate limits, admission control, and whether downstream dependencies can handle the burst. If the interviewer wants more detail, I would describe how I would test the spike behavior before production.

I would start by adding the minimum set of metrics and logs needed to understand service health and customer impact. Then I would prioritize dashboards and alerts around the most important user flows instead of trying to instrument everything at once. The reason I choose this approach is that legacy services usually need fast visibility gains before deeper observability work. I would also think about logging format consistency and whether tracing would help explain latency. If the interviewer wants more detail, I would outline a phased roadmap.