Amazon Software Engineer Interview Questions

I would first think about a brute-force approach that checks every substring, but that would be too slow. To optimize it, I would use a sliding window with a hash map or set to track the characters in the current window. As soon as I see a repeat, I would move the left pointer until the window is valid again. The reason I choose sliding window is that each character enters and leaves the window at most once, so the time complexity is O(n). The space complexity is O(min(n, alphabet size)). I would also mention edge cases like an empty string and strings with all unique characters.

I would first think about sorting each string and using the sorted form as a key, which works well as a straightforward solution. If I want to optimize for very long strings, I could instead use a character frequency signature as the hash key. I would put strings with the same signature into the same list in a hash map. The reason I choose hashing here is that anagrams share the same character composition, so grouping by a canonical representation is natural. The time complexity is typically O(n * m log m) if sorting each string of length m, or O(n * m) with frequency counts. I would also mention edge cases like empty strings and case sensitivity.

I would explain that merge sort is a divide-and-conquer algorithm. I would recursively split the array into halves until I reach single elements, then merge the sorted halves back together in order. The reason I choose merge sort is that it guarantees O(n log n) time in the worst case and is stable. The trade-off is that it needs O(n) extra space for merging. I would also mention edge cases like empty arrays, arrays of length one, and duplicate values.

I would first think about keeping all numbers sorted, but inserting into a sorted structure would be too expensive. To optimize it, I would use two heaps: a max-heap for the lower half and a min-heap for the upper half. I would keep the heaps balanced so that the median is either the top of one heap or the average of both tops. The reason I choose two heaps is that they let me insert in O(log n) and read the median in O(1). I would also mention edge cases like negative numbers and duplicate values.

Before designing it, I would clarify the file sizes, expected traffic, whether uploads must be resumable, and whether the files are public or private. At a high level, I would separate the upload API from the storage layer, store metadata in a database, and put the actual files in object storage. For large uploads, I would use multipart or chunked uploads so the client can retry failed parts without restarting everything. I would also think about antivirus scanning, content validation, and access control. The trade-off is between simplicity and resilience, but for a production system I would prioritize reliability and the ability to recover from partial failures.