Depth-First Search Questions at Meta: What to Expect

Depth-First Search (DFS) is a fundamental algorithm for traversing trees and graphs, and its prevalence at Meta is no accident. Meta's interview questions frequently involve navigating hierarchical data structures like social graphs, directory trees, or nested UI components—scenarios where DFS is the natural and efficient tool. Mastering DFS demonstrates your ability to handle recursion, backtracking, and systematic exploration, which are critical skills for building scalable systems at a company whose products are built on vast, interconnected networks. With 138 specific DFS questions in their problem bank, it's clear this is a core competency they test rigorously.

What to Expect — Types of Problems

Meta's DFS questions typically fall into three categories. First, tree traversal problems involve binary trees, N-ary trees, or trie structures. You'll be asked to find paths, calculate sums, serialize data, or validate properties. Second, graph traversal problems involve finding connected components, detecting cycles, or exploring grids (which are implicit graphs). These often model real-world networks. Third, backtracking problems are a subset of DFS where you explore all potential configurations, such as generating permutations, solving puzzles, or partitioning data. These test your ability to prune search spaces and manage state.

How to Prepare — Study Tips with One Code Example

Focus on understanding the recursive stack and state management. Practice writing both recursive and iterative (using an explicit stack) implementations. For each problem, draw the tree or graph to visualize the traversal order. A key pattern is the "Path Finder" DFS, used to check for a path or accumulate values along a path. Here is a standard recursive template for finding if a path exists from a root to a leaf in a binary tree with a given target sum.

def has_path_sum(root, target_sum):
    if not root:
        return False
    # Check if it's a leaf node and path sum matches target
    if not root.left and not root.right:
        return root.val == target_sum
    # Recurse on left and right subtrees with updated target
    new_target = target_sum - root.val
    return (has_path_sum(root.left, new_target) or
            has_path_sum(root.right, new_target))

Recommended Practice Order

Start with basic tree traversals (pre-order, in-order, post-order) to build intuition. Move to simple path-based problems, like the example above. Then, tackle graph connectivity problems on small grids. Finally, advance to complex backtracking scenarios, such as generating combinations or solving constraint-based puzzles. Always analyze time and space complexity, as interviewers will expect this. Practice explaining your recursive reasoning aloud, as communication is key during the interview.

Practice Depth-First Search at Meta