LinkedIn Interview Questions

LinkedIn needs a feature similar to Twitter feeds, where users can see recent activities of their connections. This involves multiple classes coordinating data retrieval and display.
Problem statement: Design a class TwitterFeed that handles user activity feeds and allows users to follow/unfollow other users.
- Input: Methods to postTweet(userId: int, tweet: str), getNewsFeed(userId: int), follow(followerId: int, followeeId: int), and unfollow(followerId: int, followeeId: int).
- Output: List[str] for getNewsFeed, containing up to 10 most recent tweets from the user and their followed users.
- Constraints:
- UserIds are in the range of 1 to 10^4
- Tweet content can be up to 140 characters.

To enhance performance, LinkedIn requires a caching system that follows the LRU (Least Recently Used) principle for user profile data retrieval.
Problem statement: Design and implement a class LRUCache that supports the following operations:
- get(key: int) -> int: Returns the value of the key if the key exists, otherwise return -1.
- put(key: int, value: int): Update the value of the key if the key exists, or add the key-value pair if the key does not exist. When the cache reaches its capacity, it should invalidate the least recently used item before inserting a new item.
- Input: capacity - maximum number of items the cache can hold.
- Output: int for get, no output for put.Example 1:
Input: ["LRUCache", "put", "put", "get", "put", "get", "get", "put", "get"], [[2], [1, 1], [2, 2], [1], [3, 3], [2], [4], [1], [3]]
Output: [null, null, null, 1, null, 2, -1, null, 3]
Explanation: The cache operates under the mentioned rules and examples.Constraints:
- 1 <= capacity <= 3000
- 0 <= key, value <= 10^4

LinkedIn's algorithm can identify connections between users to enhance networking. We want to find all connected components given a list of connections between user profiles.
Problem statement: Given an integer n representing the number of profiles and a list of connections edges, return the connected components across profiles.
- Input: int n, edges defined as a list of pairs representing connections.
- Output: List[List[int]] — a list that contains all connected components, with each component being a list of profile IDs.Example 1:
Input: n = 5, edges = [[0, 1], [1, 2], [3, 4]]
Output: [[0, 1, 2], [3, 4]]
Explanation: Profile 0 is connected to 1 and 2; profile 3 is connected to 4.Example 2:
Input: n = 4, edges = [[0, 1], [1, 0], [2, 3]]
Output: [[0, 1], [2, 3]]Constraints:
- 0 <= n <= 2000
- 0 <= edges.length <= n * (n - 1) / 2

LinkedIn users often write descriptive profiles, and analyzing these descriptions can help in improving user engagement. The goal is to identify the longest substring from a given profile description where no characters are repeated.
Problem statement: Given a string s, return the length of the longest substring without repeating characters.
- Input: string s
- Output: int — length of the longest substring without repeating characters.Example 1:
Input: "abcabcbb"
Output: 3
Explanation: The answer is "abc", with the length of 3.Example 2:
Input: "bbbbb"
Output: 1
Explanation: The answer is "b", with the length of 1.Constraints:
- 0 <= s.length <= 5 * 10^4
- s consists of English letters, digits, symbols, and spaces.