Microsoft Software Engineer Interview Questions

I would start by asking whether the suggestions need to be ranked by popularity, recency, or personalization. For the core data structure, I would use a trie or prefix index so I can quickly find all words that match the current prefix. Then I would layer ranking on top of that using frequency or relevance scores, because returning the right result matters as much as returning it quickly. The trade-off is between memory usage and query speed, since tries can become large if the data set is huge. I would also think about caching popular prefixes because autocomplete is usually a hot path.

I would think about checking the height of each subtree and verifying that the difference is never greater than one. A brute-force version would compute the height repeatedly for every node, but that would be inefficient. To optimize it, I would use a post-order traversal that returns both the height and whether the subtree is balanced, so each node is visited once. The reason I choose this approach is that it gives O(n) time instead of O(n^2). I would also mention edge cases like an empty tree and a single-node tree.

I would use the same core idea as an LRU cache: a hash map for fast lookup and a doubly linked list for recency tracking. When a page is accessed, I move it to the front, and when memory is full, I evict the page at the tail because that is the least recently used one. The reason I choose this structure is that both access and eviction can stay O(1). I would also mention that in an OS or storage context, I would need to think about locking and concurrency if multiple threads are touching the policy. Edge cases include capacity one and repeated access to the same page.

I would first clarify whether the system needs one-on-one chat, group chat, read receipts, typing indicators, or file sharing. At a high level, I would have an API layer for message creation, a real-time delivery layer using websockets or similar connections, and a durable message store for history and sync. I would also need fan-out logic so messages reach all intended recipients without overloading the system. The reason I keep delivery asynchronous is that users expect chat to feel instant, but the backend still needs to remain reliable under spikes. I would also mention offline support, message ordering, and retries.

I would first distinguish between expected failures and truly unexpected ones. For expected failures, I would return clear error codes and messages that the caller can act on; for unexpected failures, I would log enough context to debug the issue while avoiding sensitive data. The reason I am careful here is that good error handling improves both reliability and supportability. I would also use retries only when the operation is safe to repeat and I would add circuit breakers where a dependency is unstable. On the interview side, I would emphasize that I want the system to fail gracefully instead of failing silently or crashing the whole flow.