Binary Tree Paths (DFS)
Given the root of a binary tree, return all root-to-leaf paths as strings. Use Depth-First Search (DFS) with backtracking.
Why Interviewers Ask This
Salesforce interviewers ask this to verify your grasp of recursive traversal and state management. They specifically evaluate if you can maintain a path string while backtracking through a tree structure without creating unnecessary memory overhead. This tests your ability to handle edge cases like empty trees or single-node leaves while ensuring the solution remains clean and efficient.
How to Answer This Question
1. Clarify Requirements: Immediately define what constitutes a leaf node and confirm if the tree can be null. Salesforce values clarity, so asking about input constraints shows professionalism.
2. Define the Recursive Strategy: Explain that you will use Depth-First Search (DFS) where each call represents moving down one level. State that you need to pass the current path string to the next recursion level.
3. Implement Backtracking Logic: Detail how you append the current node's value to the path. Crucially, explain that when returning from a child call, you must remove that node's contribution (backtrack) to prepare for the next sibling branch.
4. Handle Base Cases: Explicitly mention checking for null nodes to stop recursion and identifying leaf nodes (where left and right are null) to add the completed path to your result list.
5. Optimize Space: Discuss whether you should use string concatenation in every step or a StringBuilder/char array to reduce memory allocation, demonstrating awareness of performance implications.
Key Points to Cover
- Explicitly defining the backtracking mechanism to manage path state
- Correctly identifying leaf nodes as the termination condition for paths
- Demonstrating awareness of recursion stack depth relative to tree height
- Clarifying input constraints like null roots or single-node trees upfront
- Choosing between string concatenation and mutable buffers for efficiency
Sample Answer
To solve the Binary Tree Paths problem, I would approach it using a recursive Depth-First Search strategy with explicit backtracking. First, I'd handle the base case where the root is null by returning an empty list. For the main logic, I'll create a helper function that accepts the current node and the accumulated path string. As I traverse down, I append the current node's value to the path. If I encounter a leaf node—defined as having no left or right children—I know the path is complete, so I add it to my results list. The critical part is backtracking. Since strings are immutable in many languages, passing a new string works, but conceptually, after exploring the left subtree, I must ensure the path is reset before exploring the right. In Python, since we pass references, I might use a list to build the path and join it only at the leaf, popping the last element after returning from both children. This ensures O(N) time complexity where N is the number of nodes, and space complexity is proportional to the height of the tree for the recursion stack. This approach mirrors Salesforce's focus on scalable, clean code that handles data structures efficiently without bloating memory usage.
Common Mistakes to Avoid
- Failing to backtrack correctly, causing the path string to accumulate values from previous branches
- Not handling the null root case, leading to runtime errors on empty inputs
- Confusing internal nodes with leaf nodes, resulting in incomplete or incorrect path strings
- Using global variables to store the path instead of passing state through recursion parameters
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