Amazon Software Engineer Interview Questions

I would first clarify whether the system needs strong consistency or eventual consistency across services. My default design would use an API service to accept the order, persist the state in a database, and publish an event to a durable queue or log so downstream services can react asynchronously. I would use idempotent consumers because retries and duplicate messages are inevitable in distributed systems. The reason I choose event-driven architecture is that it decouples the order flow from payment, inventory, and notification services, which improves scalability. I would also discuss failure handling, dead-letter queues, and monitoring because those are critical for production reliability.

I would first reproduce the issue and identify the shared state that is being accessed concurrently. If the critical section is small, I would use a lock or mutex; if contention is high, I would consider reducing shared state, using atomic operations, or redesigning the workflow so threads do not fight over the same data. The reason I do not jump straight to locks every time is that they can create bottlenecks and deadlocks if used carelessly. I would also mention testing with stress tools and adding observability so the problem is easier to catch in the future. The right solution depends on correctness first, then performance.

I would start by comparing metrics before and after the regression and narrowing down which endpoint or dependency changed. Then I would look at traces, logs, and database timings to isolate whether the issue is in code, infrastructure, or an external dependency. The reason I take a measurement-first approach is that latency problems can come from many layers, and guessing is usually slow and misleading. Once I find the bottleneck, I would either optimize the hot path, add caching, or reduce unnecessary calls. I would also check whether the regression is localized or affecting the whole fleet, because that changes the rollback decision.

Situation: On one project, my team wanted to ship a synchronous workflow for a feature that would trigger several downstream services, and I was concerned it would hurt reliability. Task: I needed to raise the issue without slowing the project down and still help the team make a good decision. Action: I put together a quick comparison of the synchronous approach versus an asynchronous event-driven version, including failure cases, latency impact, and the operational overhead of retries. I walked the team through a small prototype and showed that a single downstream timeout could block the user flow and create inconsistent states. Result: We switched to an async design with a status API and retry handling, and the feature launched with fewer incidents than the original plan would likely have caused.

Situation: In one release cycle, a production dashboard started showing incorrect counts, and the issue was initially assigned to another team because the symptoms appeared to be in their service. Task: I was not the primary owner, but I knew our feature depended on that data and customers were already noticing the inconsistency. Action: I jumped in, traced the request path across services, and found that our client-side retry logic was sending duplicate events under a rare timeout condition. I coordinated with the other team, wrote a fix, added idempotency keys, and stayed on the rollout until the data stabilized. Result: The bug was resolved the same day, the duplicate event rate dropped to near zero, and we added monitoring so we could catch the issue earlier next time.