LinkedIn Frontend Engineer Coding Questions

To provide an optimized news feed experience, LinkedIn needs to find the maximum engagement scores among k updates shared by users in real-time.
Given an array of integers representing engagement scores for the updates, return an array of maximum scores for each sliding window of size k.Function Signature:
- function maxSlidingWindow(scores: number[], k: number): number[]Example 1:
Input: scores = [1, 3, -1, -3, 5, 3, 6, 7], k = 3
Output: [3, 3, 5, 5, 6, 7]
Explanation: The maximum of the first three scores is 3, then 5 for the next window, and so forth.Example 2:
Input: scores = [1, -1], k = 1
Output: [1, -1]
Explanation: Each element is its own maximum in a window of size 1.Constraints:
- 1 <= scores.length <= 10^5
- -10^4 <= scores[i] <= 10^4
- 1 <= k <= scores.length

LinkedIn's frontend applications require efficient data fetching and rendering without blocking the UI thread.
Given an array of integers representing taskDurations (in milliseconds) for asynchronous tasks, simulate the behavior of the event loop to determine the total time taken to complete all tasks and print them in the order they finish. You may assume a maximum concurrency of n (number of simultaneous tasks).Function Signature:
- function simulateEventLoop(taskDurations: number[], n: number): numberExample 1:
Input: taskDurations = [200, 100, 300], n = 2
Output: 400
Explanation: The first two tasks run concurrently (200ms, 100ms), taking 200ms, followed by the 300ms task which starts after the 100ms task completes.Example 2:
Input: taskDurations = [300, 100, 200, 400], n = 2
Output: 700
Explanation: The first two tasks (300ms and 100ms) complete in 300ms, followed by 200ms starting, and then 400ms which completes after.Constraints:
- 1 <= taskDurations.length <= 1000
- 1 <= taskDurations[i] <= 10^4
- 1 <= n <= 100