OpenAI Mobile Engineer Interview Questions

With the deployment of OpenAI models on mobile devices, it's essential to efficiently find the best-performing parameters from a given set of options. Binary search provides an efficient way to narrow down optimal configurations.
Given an array of integers models representing performance scores of models, write a method that returns the index of the highest performing model using binary search.Example 1:
Input: models = [3, 4, 5, 6, 7, 8, 9]
Output: 6
Explanation: The model with score 9 is the highest score at index 6.Example 2:
Input: models = [1, 2, 3, 4, 5]
Output: 4Constraints:
- 1 <= models.length <= 10^5
- 0 <= models[i] <= 10^4

OpenAI is focused on delivering seamless mobile experiences for users interacting with its generative AI tools. Effective management of data streams while accounting for mobile constraints is critical.
Given a string s that consists of lowercase alphabets and an integer k, find the length of the longest substring that contains at most k distinct characters.Example 1:
Input: `s =

Building robust mobile applications requires careful consideration of user experience and seamless access to AI models from OpenAI, even when users are offline. This is essential for crafting resilient experiences. Your task is to design an Offline-first Sync Engine that manages data synchronization between offline mobile applications and the cloud.
Key methods to implement:
- sync(data: List<Data>): void: Syncs local data to the server when online.
- saveLocally(data: Data): void: Saves data locally when offline.
- fetchLocalData(): List<Data>: Fetches data stored on the device.
- handleConflict(remoteData: Data, localData: Data): Data: Resolves conflicts between local and remote data.Example 1:
Input: sync([{id: 1, value: 'A'}])
Output: [] (Assuming the device is online)
Example 2:
Input: saveLocally({id: 2, value: 'B'})
Output: nullConstraints:
- data.length <= 1000
- 1 <= id <= 10000

OpenAI's mobile applications frequently make requests to API endpoints, necessitating a robust caching mechanism to enhance performance and minimize unnecessary network calls.
Design and implement an LRU Cache class that stores a limited number of API responses and evicts the least-recently-used responses as new responses are added.Class definition:
- LRUCache(int capacity): Initializes the LRU cache with a given capacity.
- String get(String key): Returns the value if the key exists in the cache, otherwise returns null.
- void put(String key, String value): Updates or adds the value if the key is not already present. When the cache reaches its capacity, it should invalidate the least-recently-used item before inserting a new item.Example 1:
Input: cache = new LRUCache(2)
cache.put(1, 'A')
cache.put(2, 'B')
Output: cache.get(1) → 'A'
Example 2:
Input: cache.put(3, 'C')
Output: cache.get(2) → null Constraints:
- 1 <= capacity <= 3000
- 0 <= key <= 10^4
- 0 <= value <= 10^4