LinkedIn vs Snapchat: Interview Question Comparison

When preparing for technical interviews, company-specific question patterns reveal what each engineering team prioritizes. LinkedIn and Snapchat, while both testing core data structures, show distinct profiles in volume, difficulty, and algorithmic focus. Understanding these differences lets you tailor your preparation efficiently.

Question Volume and Difficulty

LinkedIn's tagged question pool is significantly larger and covers a broader difficulty spread. With 180 questions categorized as Easy (26), Medium (117), and Hard (37), the emphasis is clearly on Medium-tier problems. This suggests LinkedIn's interviews are designed to thoroughly assess competency in applying standard algorithms to moderately complex scenarios, with a smaller set of challenging problems to differentiate top candidates.

Snapchat's list is more concentrated at 99 questions, with a heavier weight toward Medium (62) and Hard (31) problems relative to Easy (6). This distribution indicates a process that quickly moves past fundamentals into more demanding problem-solving. The higher ratio of Hard problems implies you may encounter at least one highly complex question designed to test the limits of your analytical and coding skills under pressure.

Topic Overlap

Both companies heavily test Array, String, and Hash Table fundamentals. These are the building blocks for most problems. The key differentiator is in their preferred graph traversal method.

LinkedIn's list specifies Depth-First Search (DFS). This often correlates with problems involving recursion, backtracking, exhaustive search, or traversing hierarchical structures (like trees or nested data).

# DFS example: Clone a graph
def cloneGraph(node):
    if not node:
        return None
    visited = {}
    def dfs(original):
        if original in visited:
            return visited[original]
        clone = Node(original.val)
        visited[original] = clone
        for neighbor in original.neighbors:
            clone.neighbors.append(dfs(neighbor))
        return clone
    return dfs(node)

Snapchat's list highlights Breadth-First Search (BFS). This is typical for problems finding shortest paths, level-order traversals, or exploring states in a wavefront manner, which aligns with real-time features like messaging or story networks.

# BFS example: Shortest path in binary matrix
def shortestPathBinaryMatrix(grid):
    if grid[0][0] == 1:
        return -1
    n = len(grid)
    queue = deque([(0, 0, 1)])  # (row, col, distance)
    grid[0][0] = 1  # mark visited
    directions = [(1,0),(-1,0),(0,1),(0,-1),(1,1),(1,-1),(-1,1),(-1,-1)]
    while queue:
        r, c, dist = queue.popleft()
        if r == n-1 and c == n-1:
            return dist
        for dr, dc in directions:
            nr, nc = r + dr, c + dc
            if 0 <= nr < n and 0 <= nc < n and grid[nr][nc] == 0:
                queue.append((nr, nc, dist + 1))
                grid[nr][nc] = 1
    return -1

Which to Prepare for First

Prepare for LinkedIn first if you are earlier in your interview cycle. Its larger volume of Medium problems provides excellent, general-purpose practice for applying core data structures. Mastering this list builds a strong foundation that is transferable to many other companies, including Snapchat. Focus on arrays, strings, hash maps, and recursive DFS patterns.

Shift focus to Snapchat after solidifying fundamentals, as its list demands higher performance on harder problems. Prioritize graph algorithms, particularly BFS for shortest-path scenarios, and ensure you can handle the pressure of complex optimization. The smaller question count allows for deep, repetitive practice on high-difficulty topics.

In summary, use LinkedIn's list for breadth and foundational skill-building. Use Snapchat's list for depth and advanced problem-solving under constraints. Master the common topics first, then specialize in the preferred traversal method for each.

For targeted practice, visit the LinkedIn question list and the Snapchat question list.