Back-End Developer Interview Questions for AI Training Work

I design around the access patterns I'm confident about now, normalizing to avoid data integrity issues, while avoiding premature optimizations for query patterns that are still speculative. I'd rather adjust the schema later with a migration than over-engineer for flexibility that may never be needed.

I design the core endpoint around the common case and support optional query parameters for filtering, sorting, or field selection for more complex needs, rather than building separate endpoints for every variation. That keeps the API surface manageable while still giving flexibility to consumers with different needs.

I keep anything the client needs an immediate result from synchronous, and I move work that doesn't need to block the response, like sending a notification or processing a report, to an asynchronous job. That keeps response times fast for the user-facing part of the request while still getting the other work done.

I look for shared state being accessed without proper locking or ordering guarantees, since that's the most common source of intermittent async bugs, and I try to reproduce it under conditions that increase the likelihood of the race, like added latency or concurrent load, rather than relying on it happening naturally during testing.

I check the query plan to see whether it's actually using available indexes efficiently before assuming the query itself needs restructuring, since a missing or unused index is a very common cause of degrading performance as a table grows. I add or adjust indexes based on the actual query pattern rather than indexing preemptively.

I'd add proper error handling and alerting so failures surface immediately rather than silently, then investigate the specific inputs that were failing to understand the root cause, and I'd check whether other async jobs have the same monitoring gap, since a silent-failure pattern in one place often exists elsewhere in the codebase too.

I'd prioritize fixing the drift in the areas causing actual problems, like bugs or confusing behavior, rather than attempting a full realignment all at once, since a large-scale schema and API overhaul carries real risk of breaking existing consumers. I'd plan any breaking changes through proper API versioning.

I'd identify which parts of the system are likely to be the bottleneck under spike load, often the database or a specific synchronous endpoint, and address those specifically, using techniques like queuing non-urgent work asynchronously and caching frequently requested data, rather than assuming general infrastructure scaling alone solves it.

A background job was occasionally processing the same record twice due to a race condition between two workers picking up the same item before either had marked it complete. I added a proper locking mechanism at the point of claiming the item, which eliminated the duplicate processing, and the fix was straightforward once I'd correctly identified the actual race.

I'd initially modeled a relationship as one-to-many based on the requirements at the time, but a later feature required it to be many-to-many. I planned a migration that introduced a join table without requiring downtime, which was more involved than the original design but avoided a riskier full rewrite.