Apple Software Engineer Interview Questions

I would use a hash map for fast lookup and store an expiration time with each entry. On access, I would check whether the item has expired and remove it if necessary, and I would also need a background cleanup strategy if the cache grows large. The reason I choose this design is that it keeps reads fast while still enforcing freshness. If eviction policy matters, I could combine expiration with LRU logic. I would also mention edge cases like clock changes, zero TTL, and handling expired entries that have not yet been cleaned up.

I would first clarify whether the search is local on-device, server-side, or both. At a high level, I would index the searchable fields, keep the index updated as preferences change, and return results ranked by relevance and frequency of use. I would also think about fuzzy matching and typo tolerance, because users often do not know the exact setting name. The trade-off is between search quality and implementation complexity, so I would start with a simple indexed search and expand only if the product needs it. I would also consider privacy because settings search may reveal sensitive user data.

I would avoid division because the problem usually expects a solution that works even when zero is present. Instead, I would compute prefix products from left to right and suffix products from right to left, then multiply them together for each position. The reason I choose this approach is that it runs in O(n) time and uses only O(1) extra space besides the output array. I would also mention edge cases like one zero, multiple zeros, and negative numbers. If needed, I could explain the prefix and suffix arrays step by step on a small example.

I would first clarify which settings must sync instantly and which can tolerate delay. At a high level, I would keep a local copy on each device, send updates to a sync service, and store version metadata so the system can detect stale writes and merge conflicts. For simple settings, last-write-wins may be enough, but for richer data I would need a more careful merge strategy. The reason I choose this approach is that users expect settings to feel consistent across devices without manual intervention. I would also think about offline updates, retries, and a recovery path if two devices edit the same setting at the same time.

Before designing it, I would clarify the event volume, latency requirements, retention period, and how the data will be consumed. My high-level design would include event producers, a durable ingestion layer, stream processing for transformation, and storage systems for both raw and aggregated data. I would keep the write path lightweight so client applications do not slow down when they emit analytics. The reason I choose a pipeline design is that it decouples collection from processing, which makes the system easier to scale and recover from failures. I would also think about deduplication, schema evolution, and late-arriving events because those are common issues in real analytics systems.