Amazon Backend Engineer Interview Questions

I would make the operation idempotent and store a unique request or event ID with the work result. That way, when the same message or request arrives again, the system can detect it and avoid repeating the side effect. The reason I choose this fix is that retries are a normal part of distributed systems and duplicates must be expected. I would also think about how long to retain deduplication data and how to recover if the first attempt fails halfway through. If needed, I would explain how the design differs for writes versus read-only operations.

I would first check whether the logging path is synchronous and whether every request is writing too much detail. My first fix would be to make logging asynchronous and batch log writes so the application does less work on the critical path. The reason I choose batching is that logs should help with debugging without becoming the reason the service is slow. I would also consider sampling, log levels, and compression if the volume is still too high. If the interviewer asks, I would mention that observability needs to be balanced against runtime cost.

I would first define what 'closest' means, because it could be network latency, geography, or cost. My design would use geo-aware routing plus health checks so requests go to a nearby region that is actually able to serve traffic. The reason I choose that approach is that latency is only useful if the target region is healthy. I would also think about failover, routing consistency, and how to avoid sending traffic back and forth between regions. At scale, monitoring and clear routing rules become very important.

I would think about a trie first, because prefix search is exactly what tries are good at. If the dataset is very large, I would also consider compressing the trie or using a more compact prefix index. The reason I choose a trie is that it gives fast lookup for prefix-based queries and keeps the search path simple. I would also discuss ranking metadata if the results need to be ordered by relevance or popularity. The trade-off is memory usage, since naive tries can become expensive at scale.

I would first check whether the workers are underprovisioned, whether the tasks are too heavy, or whether the queue has become a bottleneck because of downstream slowness. Then I would decide whether to add more consumers, split the work into smaller tasks, or slow incoming traffic with backpressure. The reason I choose this approach is that queue growth is usually a capacity mismatch, not just a queue problem. I would also think about priority queues, dead-letter handling, and whether some jobs can be delayed or dropped safely. If the interviewer wants more detail, I would explain how I would monitor queue lag and worker throughput over time.