NVIDIA Software Engineer Interview Questions

I would first think about the simplest version, which is a circular array with head and tail pointers. That gives the basic structure, but because this is thread-safe and high-throughput, I would then think about how many producers and consumers there are, because that changes the locking strategy. If it is a single-producer single-consumer setup, I could use atomic indices and avoid heavy locking, but if multiple threads can write or read, I would likely use a mutex or a lock-free queue only if I am confident the complexity is worth it. The reason I choose a ring buffer is that it has predictable memory usage and good cache locality, which matters a lot in performance-sensitive systems. I would also mention edge cases like buffer full, buffer empty, wraparound, and making sure reads do not see partially written data. The time complexity for push and pop is O(1), and the space complexity is O(capacity).

I would first think about creating a second matrix and writing each value into its rotated position, which is the easiest way to reason about it. To optimize space, I would transpose the matrix first and then reverse each row. The reason I choose this approach is that it works in place and keeps the implementation clean. The time complexity is O(n^2), since every element must be touched, and the space complexity is O(1) if I mutate the matrix directly. I would also mention that this only works cleanly for square matrices, so I would clarify that requirement up front. Edge cases include a 1 by 1 matrix and an empty matrix.

I would first think about a simple approach using a list and scanning for keys, but that would be too slow. To optimize it, I would use a hash map for O(1) lookup and a doubly linked list to track recency. When a key is accessed, I move it to the front, and when the cache is full, I evict the node at the tail because that is the least recently used one. The reason I choose this design is that it gives O(1) get and put operations, which is the standard expectation for this problem. I would also mention edge cases like updating an existing key, capacity one, and repeated gets on the same item.

I would start by sorting the array and taking the last K elements, which is the easiest brute-force style answer. To optimize it, I would use a min-heap of size K so I only keep the K largest values seen so far. The reason I choose a min-heap is that it keeps memory low and gives good performance when K is much smaller than N. This gives O(n log k) time and O(k) extra space. I would also mention that if the interviewer wants the elements in sorted order, I can sort the final K results at the end.

I would first think about what resources the threads are waiting on and whether there is a circular dependency. If I am looking at a resource graph, I would model threads and locks as nodes and check for a cycle. The reason I focus on the cycle is that a deadlock requires circular wait, so cycle detection becomes the key idea. In a practical system, I would also look at lock acquisition order, timeouts, and whether any thread is holding one lock while waiting on another. I would mention that the exact detection strategy depends on whether this is a theoretical interview problem or a production debugging question.