Meta Backend Engineer Interview Questions

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 a compressed trie or another 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 popularity or relevance. 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 downstream services are slowing everything down. Then I would decide whether to add more consumers, split work into smaller tasks, or slow incoming traffic with backpressure. The reason I choose this approach is that queue growth usually means a capacity mismatch rather than a queue problem by itself. I would also think about priorities, dead-letter handling, and whether some jobs can be delayed or dropped safely. If needed, I would explain how I would monitor queue lag and worker throughput.

I would make the operation idempotent by using a client-generated key or request identifier. The server would store that key with the operation result or status, and any retry with the same key would return the same outcome. The reason I choose this design is that retries are unavoidable when networks are unreliable. I would also discuss expiration of stored keys, partial failure handling, and what happens if the first attempt succeeds but the response is lost. This pattern is especially useful for payments, order creation, and other state-changing actions.

I would first verify how the cache is being invalidated and whether the TTL is too long for the data. Then I would look at whether writes are updating the source of truth but not the cache, or whether the cache is being refreshed too slowly. The reason I start there is that stale cache problems usually come from bad invalidation rules rather than the cache itself. I would also think about whether stale reads are actually acceptable in the product. If not, I would consider tighter TTLs or a different cache strategy.

I would first reproduce the crash with a load test and inspect memory, CPU, logs, and stack traces. Heavy-load crashes often come from resource exhaustion, thread contention, or a bug that only shows up when timing changes. The reason I choose this path is that load-related issues are easier to solve once I know whether the failure is memory, concurrency, or logic related. I would also think about whether the worker needs better backpressure or smaller batches. After finding the root cause, I would add monitoring and a regression test.