Tree Questions at LinkedIn: What to Expect

Tree questions appear in roughly 11% of LinkedIn's technical interview question pool (20 out of 180). This frequency reflects the importance of hierarchical data structures in modeling real-world relationships—the core of LinkedIn's professional network, organizational charts, and content hierarchies. Success with tree problems demonstrates your ability to handle recursive logic, manage state during traversal, and solve problems with optimal time and space complexity, which are critical skills for backend and data-intensive roles at the company.

What to Expect — Types of Problems

LinkedIn's tree questions typically focus on binary trees and binary search trees (BSTs), with occasional n-ary tree problems. You can expect the following categories:

1. Traversal Variations: Problems that require more than a standard in-order, pre-order, or post-order traversal. This includes level-order traversal (BFS), zigzag traversal, and boundary traversal.
2. Path & Sum Problems: Questions that ask you to find paths meeting specific criteria (e.g., root-to-leaf paths that sum to a target value) or compute sums (e.g., maximum path sum, sum of left leaves).
3. BST Property & Operations: Validating a BST, finding the lowest common ancestor (LCA) in a BST, inserting/deleting nodes, or recovering a corrupted BST.
4. Ancestor & Relationship Problems: Finding the LCA in a standard binary tree (with or without parent pointers) or checking node relationships (e.g., cousins in a binary tree).
5. Construction & Conversion: Building a tree from traversal outputs (e.g., from inorder and preorder arrays) or converting a tree into another structure (e.g., flattening a tree to a linked list).

How to Prepare — Study Tips with One Code Example

Master the fundamentals first: implement all standard traversals (recursive and iterative) from memory. Then, focus on pattern recognition. Most tree problems are solved using Depth-First Search (DFS) or Breadth-First Search (BFS). For DFS, practice writing helper functions that pass state (like the current path or running sum) down the recursion and return aggregated results upward.

A key pattern is the recursive DFS function that returns a value. This is used in problems like finding maximum depth, checking for balance, or computing diameter.

def maxDepth(root):
    if not root:
        return 0
    left_depth = maxDepth(root.left)
    right_depth = maxDepth(root.right)
    return max(left_depth, right_depth) + 1

Recommended Practice Order

1. Foundations: Start with basic traversals (pre-order, in-order, post-order, level-order) and fundamental computations (max depth, node count).
2. Path & Sum: Move to problems involving paths and sums (path sum, root-to-leaf paths, maximum path sum).
3. BST Properties: Tackle BST-specific problems (validation, LCA in BST, search/insert).
4. Construction & Conversion: Practice building trees from arrays or other traversals.
5. Advanced Problems: Finally, attempt complex problems like serialization/deserialization, flattening, and recovering a BST.

This progression builds the recursive thinking needed for the most common LinkedIn tree interview questions.

Practice Tree at LinkedIn