Amazon Backend Engineer Interview Questions

I would make the processing idempotent and keep track of whether a job has already been completed. That can be done with a status record, a processed-job table, or a transactional write before acknowledging the queue message. The reason I choose this approach is that retries are normal and the system should handle them without duplicating side effects. I would also think about retry limits and a dead-letter queue for repeated failures. If the interviewer wants comparison, I would explain the difference between at-least-once and exactly-once semantics.

I would define the activity metric first, because 'top users' can mean different things depending on the product. My pipeline would stream events into a counter service, compute rolling-window aggregates, and keep the top results in a small ranking structure. The reason I choose this design is that it keeps the system fast without requiring full recomputation every minute. I would also think about partitioning, late events, and how to merge partial aggregates across workers. If the interviewer asks, I would mention whether the top list should be global or per region.

I would first verify whether the hot record is causing database contention, cache misses, or lock pressure. If the same record is read often, I would add caching or a read replica strategy depending on whether the data can be slightly stale. The reason I choose that path is that hotspot traffic is a very common backend problem and caching often gives the biggest win. I would also think about sharding, request coalescing, and whether the data model itself is causing the hotspot. The answer would depend on whether the read or write path is the real bottleneck.

I would inspect the job payloads, stack trace, and any recent code or schema changes. Then I would reproduce the crash locally or in a staging environment with a small failing example. The reason I start there is that worker bugs often come from malformed input or an edge case that only shows up in production data. I would also think about whether the job should be retried, skipped, or sent to a dead-letter queue. Once I find the root cause, I would add better validation or safer error handling.

I would first identify the bottleneck instead of guessing. The key areas I would check are statelessness, database pressure, caching, connection limits, and whether the service can scale horizontally. The reason I start there is that concurrency issues usually come from shared resources rather than the handler itself. I would also think about load shedding and graceful degradation if traffic exceeds capacity. If the interviewer wants a concrete plan, I would say I would make the service stateless first, then protect the database, then add caching and queues where needed.