Apple Backend Engineer Interview Questions

I would try to reproduce the issue with a large test job and inspect memory use, CPU, logs, and stack traces. Large-job crashes often come from resource exhaustion or an algorithm that does not scale well enough for the input size. The reason I start there is that the crash pattern itself usually points to the failing resource. I would also think about splitting the work into smaller chunks or adding backpressure. After I fix the bug, I would add monitoring and a regression test with a similarly large input.

I would first ask whether sessions must survive server restarts and how quickly they need to expire. One option is to store sessions in a shared cache or database so any server can validate them. Another option is stateless tokens if the session payload is small and revocation needs are manageable. The reason I compare both is that they solve different trade-offs between simplicity, scalability, and invalidation. I would also think about token rotation, expiration, and what happens if the shared store is unavailable.

I would assume duplicates are part of the system and make the consumer idempotent. That means storing enough state to tell whether a message has already been processed, such as a processed-message table or a deduplication record. The reason I choose this approach is that message duplication is common in retries and restarts. I would also think about retention for that state and how to recover if processing fails halfway through. If needed, I would explain the difference between at-least-once and exactly-once delivery guarantees.

I would add logs, metrics, and traces, but I would make sure they each answer a different question. Metrics show system health, logs show detailed events, and traces show the request path across services. The reason I choose all three is that production debugging usually needs both high-level signals and low-level context. I would also think about correlation IDs, dashboards, and alert thresholds. If the interviewer asks, I would mention that too much noisy logging can be as bad as too little observability.

I would first find the bottleneck rather than assuming the answer is just to add more servers. I would inspect the database, cache, queueing, connection limits, and whether the service can scale horizontally. The reason I start there is that traffic issues usually come from one constrained resource, not the whole application. I would also think about load shedding and graceful degradation so the system stays partially usable under stress. If the interviewer wants a concrete plan, I would say I would make the service stateless, protect the database, then add caching and queueing where needed.