Binary trees are fundamental data structures widely used in computer science and software development. Understanding their properties and efficiently solving problems related to binary trees is crucial for acing coding interviews. In this article, we’ll explore 20 common questions about binary trees that frequently appear in coding interviews and provide tips and example code to help you master these concepts.

**1. What is a Binary Tree?**

A binary tree is a hierarchical data structure consisting of nodes, where each node has at most two children, referred to as the left child and the right child.

**2. Difference between Binary Tree and Binary Search Tree (BST)?**

A Binary Search Tree (BST) is a type of binary tree where the left child is smaller than the parent, and the right child is larger. This property ensures efficient searching in logarithmic time.

**3. How to traverse a Binary Tree in-order, pre-order, and post-order?**

**In-order:**Left-Root-Right**Pre-order:**Root-Left-Right**Post-order:**Left-Right-Root

**4. Write code for in-order traversal of a Binary Tree.**

```
class TreeNode:
def __init__(self, value):
self.value = value
self.left = None
self.right = None
def in_order_traversal(node):
if node:
in_order_traversal(node.left)
print(node.value)
in_order_traversal(node.right)
```

**5. Find the height (depth) of a Binary Tree.**

```
def tree_height(node):
if not node:
return 0
left_height = tree_height(node.left)
right_height = tree_height(node.right)
return 1 + max(left_height, right_height)
```

**6. Check if a Binary Tree is balanced.**

```
def is_balanced(node):
if not node:
return True
left_height = tree_height(node.left)
right_height = tree_height(node.right)
if abs(left_height - right_height) > 1:
return False
return is_balanced(node.left) and is_balanced(node.right)
```

**7. Find the lowest common ancestor in a Binary Tree.**

```
def lowest_common_ancestor(root, p, q):
if not root or root == p or root == q:
return root
left = lowest_common_ancestor(root.left, p, q)
right = lowest_common_ancestor(root.right, p, q)
return root if left and right else left or right
```

**8. Check if a Binary Tree is a valid Binary Search Tree.**

```
def is_valid_BST(root, min_val=float('-inf'), max_val=float('inf')):
if not root:
return True
if not (min_val < root.value < max_val):
return False
return (is_valid_BST(root.left, min_val, root.value) and
is_valid_BST(root.right, root.value, max_val))
```

**9. Serialize and deserialize a Binary Tree.**

Serialization is the process of converting a data structure or object into a sequence of bits so that it can be easily stored or transmitted.

```
def serialize(root):
if not root:
return "null"
return str(root.value) + "," + serialize(root.left) + "," + serialize(root.right)
def deserialize(data):
nodes = data.split(",")
return build_tree(nodes)
def build_tree(nodes):
val = nodes.pop(0)
if val == "null":
return None
node = TreeNode(int(val))
node.left = build_tree(nodes)
node.right = build_tree(nodes)
return node
```

**10. Find the diameter of a Binary Tree.**

The diameter of a binary tree is the length of the longest path between any two nodes.

```
def diameter_of_binary_tree(root):
def height(node):
if not node:
return 0
left_height = height(node.left)
right_height = height(node.right)
diameter_of_binary_tree.result = max(
diameter_of_binary_tree.result, left_height + right_height
)
return 1 + max(left_height, right_height)
diameter_of_binary_tree.result = 0
height(root)
return diameter_of_binary_tree.result
```

These are just a few examples of the many questions related to binary trees that you might encounter in coding interviews. Remember to practice writing code on a whiteboard or paper and explain your thought process clearly. Understanding the underlying principles and being able to write clean and efficient code is key to success in coding interviews. Happy coding!