Depth-First Search Questions at Flipkart: What to Expect

Depth-First Search (DFS) is a core algorithmic technique for traversing trees and graphs, and its prevalence at Flipkart signals what the company values in engineers. With 20 out of 117 total coding questions tagged with DFS, it's one of their most frequently assessed topics. Flipkart's technical interviews focus heavily on problem-solving for large-scale systems, e-commerce logic, and data processing. DFS is not just an algorithm to memorize; it's a fundamental pattern for exploring state spaces, which directly applies to real-world scenarios like inventory management systems (traversing category trees), recommendation engines (exploring user-item graphs), and dependency resolution in microservices. Mastering DFS demonstrates your ability to handle recursive problem decomposition and backtracking—skills critical for building and debugging complex software.

What to Expect — Types of Problems

Flipkart's DFS questions typically fall into a few key categories that blend algorithmic thinking with practical application.

Tree Traversal & Manipulation: Expect problems involving binary trees, N-ary trees (like product category hierarchies), and serialization/deserialization. Questions may ask you to find paths, calculate sums, or validate properties.

Graph Exploration & Pathfinding: These problems involve grids (matrices) or explicit graph representations. Common themes include finding connected components, detecting cycles, or exploring all possible states (e.g., finding all combinations of offers applicable to a cart).

Backtracking & Combinatorial Search: This is a major application of DFS. You'll encounter classic puzzles and generation problems, such as finding all permutations of a set, solving Sudoku-style constraints, or generating all valid parentheses combinations. These test your ability to prune invalid states efficiently.

Recursive Problem Decomposition: Some problems use DFS as a framework for divide-and-conquer, like calculating expressions or solving problems that require exploring all decision branches.

How to Prepare — Study Tips with One Code Example

Start by solidifying the core recursive and iterative implementations of DFS for both trees and graphs. Understand the implicit call stack in recursion and how to simulate it with an explicit stack. For graph problems, always clarify if the graph is directed/undirected and how to handle visited states to avoid infinite loops. Practice drawing the recursion tree for backtracking problems to visualize state space.

A key pattern is using DFS to find all paths in a tree that meet a condition, such as paths where the sum of node values equals a target. This combines traversal with backtracking state management.

def path_sum(root, target_sum):
    def dfs(node, current_path, current_sum):
        if not node:
            return
        current_path.append(node.val)
        current_sum += node.val
        # Check at leaf node
        if not node.left and not node.right and current_sum == target_sum:
            result.append(list(current_path))
        dfs(node.left, current_path, current_sum)
        dfs(node.right, current_path, current_sum)
        # Backtrack
        current_path.pop()
    result = []
    dfs(root, [], 0)
    return result

Recommended Practice Order

Build competence progressively. First, master standard traversals (pre-order, in-order, post-order) on binary trees. Next, tackle fundamental graph DFS problems like number of islands or clone graph. Then, move to backtracking classics: permutations, subsets, and combination sum. Finally, integrate these concepts in more complex scenarios, such as solving puzzles (N-Queens, Sudoku solver) or pathfinding with constraints. Always analyze time and space complexity, focusing on the recursion depth and branching factor.

Practice Depth-First Search at Flipkart