Apple Backend Engineer Interview Questions

I would first clarify whether the limit is per user, per API key, per IP, or per endpoint, because that changes the storage model and the enforcement point. My default choice would be a token bucket because it allows short bursts while still enforcing a long-term limit. I would place the limiter as close to the edge as possible so the backend does not spend resources on requests that should be rejected. The reason I choose this approach is that it is practical, scalable, and easy to reason about during real traffic spikes. I would also discuss where the counter state lives, how to keep it consistent across servers, and what happens if the limiter store fails.

I would say REST is usually simpler when the API has a small number of clear resources and the client needs predictable responses. GraphQL becomes attractive when clients need flexible queries and want to avoid over-fetching or under-fetching data. The reason I compare them this way is that the decision usually comes down to access patterns rather than trendiness. I would also mention that GraphQL can add server-side complexity, especially around caching, authorization, and query depth control. In an interview, I would tie the choice back to the product need instead of treating one as universally better.

I would aim for backward-compatible changes first so old and new code can run together during the rollout. A common strategy is to add new columns or tables, deploy code that writes to both old and new paths if needed, backfill data, and then switch reads before cleaning up the old schema. The reason I choose this phased approach is that it lowers the risk of downtime and makes rollback much safer. I would also think about locking, migration size, and whether the migration can be done online. If the interviewer asks, I would explain how I would test the migration on a staging copy of production data.

I would explain that in the presence of a network partition, a distributed system has to trade off between consistency and availability. In practice, that means the system cannot always guarantee both at the same time when parts of the network fail. The reason I bring this up is that many backend design decisions are really about which trade-off is acceptable for the product. I would then connect it to the specific use case, such as choosing stronger consistency for money movement and higher availability for less critical reads. I would avoid making it sound purely academic and instead tie it back to real system behavior.

I would first identify who is allowed to call the endpoint and what data they should be able to access. Then I would add authentication, authorization, input validation, rate limiting, and transport security. The reason I choose this layered approach is that security failures often happen when one control is missing rather than because of one giant mistake. I would also mention logging and audit trails so suspicious activity can be investigated later. If the interviewer wants more depth, I would talk about token expiration, least privilege, and protection against common injection attacks.