OpenAI Software Engineer System Design Questions

OpenAI often needs to compute the median of predicted metrics in real-time. This requires an efficient data structure to retrieve the median quickly while processing continuous streams of data.
### Class Definition
Class MedianFinder should implement the following methods:
- addNum(num: int) -> None: Adds a number to the data structure.
- findMedian() -> float: Returns the median of all added numbers.### Example 1:
Input: medianFinder = MedianFinder(); medianFinder.addNum(1); medianFinder.addNum(2); medianFinder.findMedian()
Output: 1.5
Explanation: The median of [1,2] is 1.5### Example 2:
Input: medianFinder.addNum(3); medianFinder.findMedian()
Output: 2
Explanation: The median of [1,2,3] is 2.### Constraints:
- -10^5 <= num <= 10^5
- There will be at most 1,000,000 calls to addNum and findMedian.

OpenAI needs an efficient caching system to store previously computed model predictions, optimizing resource usage and response time.
### Class Definition
Class LRUCache should implement the following methods:
- get(key: int) -> int: Returns the value of the key if the key exists, otherwise returns -1.
- put(key: int, value: int) -> None: Updates the value of the key if the key exists existing and moves the key to the front of the cache. Otherwise, it adds the key/value pair to the cache.### Example 1:
Input: cache = LRUCache(2); cache.put(1, 1); cache.put(2, 2); cache.get(1)
Output: 1
Explanation: Returns 1### Example 2:
Input: cache.put(3, 3); cache.get(2)
Output: -1
Explanation: Returns -1 since the cache reached its capacity; LRU (key = 2) was evicted.### Constraints:
- The capacity of the cache will be between 1 and 1000.
- All keys will be unique.