Depth-First Search Questions at Zepto: What to Expect

Depth-First Search (DFS) is a core algorithm for navigating graphs and trees, making it essential for solving problems involving hierarchical data, paths, and connected components. At Zepto, a fast-growing quick commerce platform, DFS appears in roughly 18% of their technical interview questions (5 out of 28). This frequency reflects the company's need to handle complex data structures inherent to their systems—such as mapping delivery routes, modeling product categories, or analyzing user session flows. Mastering DFS demonstrates your ability to think recursively and systematically explore state spaces, a skill directly applicable to optimizing Zepto's logistics and catalog management.

What to Expect — Types of Problems

Zepto's DFS questions typically fall into three categories. First, tree traversal problems are common, where you might validate a binary search tree, find the lowest common ancestor, or compute path sums. These test your understanding of hierarchical data, similar to navigating a product taxonomy. Second, graph pathfinding and connectivity questions appear, such as counting islands in a grid (analogous to mapping delivery zones) or finding if a path exists between nodes (like checking connectivity in a network of dark stores). Third, you may encounter backtracking problems, which are DFS applied to generate permutations, solve puzzles, or explore all possible configurations—useful for scenarios like scheduling delivery slots or optimizing warehouse layouts.

How to Prepare — Study Tips with One Code Example

Focus on the recursive and iterative implementations of DFS. For trees, practice pre-order, in-order, and post-order traversals. For graphs, remember to track visited nodes to avoid cycles. A key pattern is using a helper function for recursion, which keeps your main function clean. Always clarify the problem's constraints (e.g., graph size, recursion depth) first. Below is a template for a recursive DFS to traverse all nodes in a graph represented as an adjacency list, a pattern you can adapt for many problems.

def dfs_graph(node, graph, visited):
    if node in visited:
        return
    visited.add(node)
    # Process node here if needed
    for neighbor in graph[node]:
        dfs_graph(neighbor, graph, visited)

# Example usage for a graph starting at node 0
graph = {0: [1, 2], 1: [2], 2: [0, 3], 3: [3]}
visited = set()
dfs_graph(0, graph, visited)

Recommended Practice Order

Start with basic tree traversals to build intuition. Move to simple graph problems like counting connected components. Then tackle backtracking challenges, such as generating subsets. Finally, combine DFS with other concepts like memoization for dynamic programming or pruning for optimization. Practice on Zepto-specific problems to familiarize yourself with their problem style. Time yourself to simulate interview conditions.

Practice Depth-First Search at Zepto