LinkedIn vs Coupang: Interview Question Comparison

When preparing for technical interviews, understanding the specific focus of each company can dramatically improve your efficiency. LinkedIn and Coupang, while both major tech employers, present distinct interview landscapes in terms of volume, difficulty, and core topics. This comparison breaks down their question patterns to help you prioritize your study.

Question Volume and Difficulty

The sheer number of reported questions is the most immediate difference. LinkedIn's list of 180 questions (Easy: 26, Medium: 117, Hard: 37) is over three times larger than Coupang's 53 questions (Easy: 3, Medium: 36, Hard: 14). This disparity signals a key strategic point: for LinkedIn, you are preparing for a broader, more established question bank where pattern recognition across many problems is crucial. The heavy weighting toward Medium difficulty for both companies is standard, but LinkedIn's significant pool of Hard questions suggests you may encounter more complex algorithmic twists.

For Coupang, the list is more contained. The low number of Easy questions and high proportion of Medium and Hard problems indicate that their interviews are consistently challenging from the outset. Preparing for Coupang can be more focused, but requires deep mastery of the listed topics.

Topic Overlap

Both companies heavily test Array, String, and Hash Table fundamentals. These are the building blocks for most algorithms and are non-negotiable areas of study.

The critical divergence is in the advanced topics. LinkedIn shows a notable emphasis on Depth-First Search (DFS), pointing toward a strong focus on graph and tree traversal problems, which are common in modeling networks and hierarchies.

Coupang, conversely, highlights Dynamic Programming (DP). This indicates a preference for problems involving optimization, counting, or complex decision-making, which are highly relevant in e-commerce and logistics contexts like Coupang's core business.

Here is a basic example illustrating the difference in focus:

# LinkedIn-style: DFS on a graph (Number of Islands)
def numIslands(grid):
    def dfs(r, c):
        if not (0 <= r < len(grid)) or not (0 <= c < len(grid[0])) or grid[r][c] != '1':
            return
        grid[r][c] = '0'
        for dr, dc in [(1,0),(-1,0),(0,1),(0,-1)]:
            dfs(r+dr, c+dc)

    count = 0
    for r in range(len(grid)):
        for c in range(len(grid[0])):
            if grid[r][c] == '1':
                dfs(r, c)
                count += 1
    return count

# Coupang-style: Dynamic Programming (Climbing Stairs)
def climbStairs(n):
    if n <= 2:
        return n
    dp = [0] * (n + 1)
    dp[1], dp[2] = 1, 2
    for i in range(3, n + 1):
        dp[i] = dp[i-1] + dp[i-2]
    return dp[n]

Which to Prepare for First

Your preparation order should be guided by your timeline and the breadth of your target companies.

Prepare for Coupang first if your interview is imminent or you are specifically targeting e-commerce or logistics firms. The smaller question bank allows for a deep, concentrated study session. Master the core topics (Array, String, Hash Table) and then drill intensely on Dynamic Programming patterns. This focused approach can yield high returns in a shorter timeframe.

Prepare for LinkedIn first if you have more time or are casting a wider net across general tech companies. The vast question list will force you to build robust pattern recognition across a wider variety of problems, including DFS. This broader foundation will inherently cover the core topics needed for Coupang, making a subsequent switch to focused DP practice relatively straightforward. Starting with LinkedIn's list builds a more versatile algorithmic foundation.

In essence, begin with the company whose preparation style best matches your overall goals: focused depth (Coupang) or broad foundational strength (LinkedIn).

For more detailed question lists, visit the LinkedIn and Coupang pages on CodeJeet.