Apple Backend Engineer Interview Questions

I would first clarify whether the flags are for experiments, gradual rollout, kill switches, or all of those. My design would include a central config store, a fast local cache on application servers, and a rules engine that decides which users see which values. The reason I choose this structure is that flag checks need to stay lightweight while still being flexible. I would also think about audit logs, rollbacks, and stale flags that need cleanup. If the interviewer asks, I would talk about percentage rollout and targeting by user attributes.

I would make message writes durable before acknowledging success to the producer. On the consumer side, I would keep retry behavior and acknowledgements carefully defined so a crash does not lose work. The reason I choose this approach is that data loss is usually unacceptable in backend messaging systems. I would also discuss replication, persistence, and recovery after partial outages. If the interviewer wants more depth, I would explain how I would test failure scenarios like broker restarts and network partitions.

I would separate the operations by business importance. For things like money movement or state transitions that must be correct, I would favor stronger consistency even if availability is reduced during failures. For less critical reads, I would lean toward availability and accept temporary staleness. The reason I frame it this way is that backend systems usually need different guarantees for different workflows. I would also mention that the right answer depends on what failure is more expensive: showing stale data or refusing the request.

I would first find the biggest bottleneck instead of assuming every layer needs to be rebuilt. Then I would look at statelessness, database pressure, caching, queueing, and connection limits. The reason I start there is that large-scale systems usually fail at shared resources before they fail at request handling. I would also think about load shedding, graceful degradation, and horizontal scaling. If the interviewer wants a concrete plan, I would say I would make the service stateless first and then protect the database and cache layers.

I would first confirm whether the hot row is creating lock contention, cache misses, or write amplification. If the issue is read-heavy, I might cache the value or use a read replica if freshness allows it. If the issue is write-heavy, I might shard the data, redesign the schema, or reduce contention through batching. The reason I choose this approach is that hot rows are often a sign of a data model problem rather than just a tuning problem. I would also mention request coalescing and whether the access pattern can be changed.