# Solving Leetcode Interviews in Seconds with AI: Find Subsequence of Length K With the Largest Sum


	# Introduction
	In this blog post, we will explore how to solve the LeetCode problem "2099" using AI. LeetCode is a popular platform for preparing for coding interviews, and with the help of AI tools like [Chatmagic](https://www.chatmagic.app), we can generate solutions quickly and efficiently - helping you pass the interviews and get the job offer without having to study for months.

	# Problem Statement
	> You are given an integer array nums and an integer k. You want to find a subsequence of nums of length k that has the largest sum. Return any such subsequence as an integer array of length k. A subsequence is an array that can be derived from another array by deleting some or no elements without changing the order of the remaining elements.   Example 1:  Input: nums = [2,1,3,3], k = 2 Output: [3,3] Explanation: The subsequence has the largest sum of 3 + 3 = 6. Example 2:  Input: nums = [-1,-2,3,4], k = 3 Output: [-1,3,4] Explanation:  The subsequence has the largest sum of -1 + 3 + 4 = 6.  Example 3:  Input: nums = [3,4,3,3], k = 2 Output: [3,4] Explanation: The subsequence has the largest sum of 3 + 4 = 7.  Another possible subsequence is [4, 3].    Constraints:  1 <= nums.length <= 1000 -105 <= nums[i] <= 105 1 <= k <= nums.length  

	# Explanation
	Here's a breakdown of the solution:

*   **Select Top k Elements:** Identify the *k* largest elements in the input array *nums*. This can be done efficiently using techniques like `heapq.nlargest` in Python or sorting.
*   **Maintain Original Order:** Because the problem asks for a *subsequence*, the relative order of elements in the original array must be preserved. We achieve this by iterating through the original `nums` array and including the top *k* elements in the correct order.
*   **Handle Duplicates:** The solution needs to handle duplicate values correctly. A counter is used to track the remaining number of times each of the top *k* elements should be included in the subsequence.

*   **Time & Space Complexity:** O(n log k) time complexity and O(n) space complexity.

	
	# Code
	```python
	import heapq
from collections import Counter

def maxSubsequence(nums, k):
    """
    Finds a subsequence of nums of length k that has the largest sum.

    Args:
        nums: An integer array.
        k: The desired length of the subsequence.

    Returns:
        An integer array representing the subsequence.
    """

    top_k_elements = heapq.nlargest(k, nums)
    counts = Counter(top_k_elements)
    result = []

    for num in nums:
        if num in counts and counts[num] > 0:
            result.append(num)
            counts[num] -= 1
            if len(result) == k:
                break

    return result
	```