Approach

To effectively answer the interview question on implementing an algorithm to determine if a binary tree is height-balanced, follow this structured framework:

1. Understand the Definition: A height-balanced binary tree is defined as a tree where the heights of the two child subtrees of any node differ by no more than one.

2. Choose the Right Algorithm: Decide between a recursive approach or an iterative one. The recursive approach is often more intuitive for tree problems.

3. Outline the Steps:

• Create a helper function to calculate the height of the tree.

• Check the balance condition at each node.

• Return results in a way that efficiently tracks height and balance.

• Consider Edge Cases: Think about how to handle empty trees or trees with only one node.

• Optimize for Performance: Ensure the implementation runs in O(n) time complexity, where n is the number of nodes in the tree.

Key Points

• Definition Clarity: Be clear on what constitutes a height-balanced tree.

• Algorithm Choice: Recursive methods are generally preferred for tree traversal.

• Efficiency: Aim for a solution that checks balance while calculating height, avoiding duplicate traversals.

• Edge Cases: Always address potential edge cases in your explanation.

Standard Response

To determine if a binary tree is height-balanced, I would implement a recursive algorithm that checks the height of subtrees and their balance conditions. Here’s how I would approach this:

class TreeNode:
 def __init__(self, val=0, left=None, right=None):
 self.val = val
 self.left = left
 self.right = right

def isBalanced(root: TreeNode) -> bool:
 def check_balance(node: TreeNode) -> int:
 if not node:
 return 0 # Base case: the height of an empty tree is 0

 left_height = check_balance(node.left)
 if left_height == -1: # Left subtree is not balanced
 return -1

 right_height = check_balance(node.right)
 if right_height == -1: # Right subtree is not balanced
 return -1

 # Check the balance condition
 if abs(left_height - right_height) > 1:
 return -1 # Not balanced

 # Return the height of the tree
 return max(left_height, right_height) + 1

 return check_balance(root) != -1 # The tree is balanced if we don't return -1

• The check_balance function returns the height of the tree if it is balanced.

• If any subtree is found to be unbalanced (returns -1), the main function will also return false.

• This implementation ensures we only traverse each node once, maintaining O(n) time complexity.

• Explanation:

Tips & Variations

Common Mistakes to Avoid

• Ignoring Edge Cases: Failing to consider empty trees or single-node trees can lead to incorrect conclusions.

• Incorrect Balance Check: Miscalculating the height or the balance condition may yield wrong results.

• Not Returning Early: Failing to stop further checks once an imbalance is found can lead to unnecessary computations.

Alternative Ways to Answer

• Iterative Approach: Although less common for this type of problem, you can discuss how to use a stack for an iterative traversal of the tree.

• Depth-First Search (DFS): Highlight the DFS method as another way to explore tree structures.

Role-Specific Variations

• Technical Roles: Emphasize performance optimization and edge cases more rigorously.

• Managerial Roles: Focus on how you would guide a team through similar algorithm implementations and what best practices you would recommend.

• Creative Roles: Discuss how algorithmic thinking can apply to problem-solving in design or creative projects.

Follow-Up Questions

• Can you explain how the algorithm would change if we allowed for a different balance factor?

• How would you modify your approach if the tree contained additional constraints, such as being a binary search tree?

• What would be the impact of the balancing check on the performance of other operations in a tree structure?

By preparing your answer with this comprehensive and structured approach, you will demonstrate your technical knowledge and problem-solving capabilities effectively during the interview