Data structures and algorithms are the key to crack coding interviews and land your dream job!
From stacks to queues to binary trees to arrays, this concept holds several sub concepts, each having some sort of unique properties.
Binary trees are touted to be an essential DSA concept whose questions often appear in the technical interviews.
Tree questions are crucial yet uncertain and range from medium to high level of difficulty. Hence, they demand rigorous preparation.
To give your preparation a better head start, we have come up with this blog post!
Here, learn about the most common binary tree questions asked in the interview to help you gain knowledge momentum.
Let’s get started!
Binary tree interview questions
Binary tree is touted as the essential data structure where each of the nodes have at most two present children which are called as the left or right child. This concept is quite wide and includes a variety of other concepts in it.
Having a strong foundation on the concept of binary tree is crucial for the candidates as the tree questions can come alongside other problems like queue problems, sql problems or array related problems.
Here, in this blog we have combined some of the vital binary tree questions that are quite commonly asked from the candidates in an interview.
Explain different types of binary tree
There are mainly three types of binary tree in the data structure. In a full binary tree, all nodes have two child nodes. Though, in the complete binary tree, all levels will be filled except for the last one. Moreover, in the perfect binary tree, all its nodes will have two children with all the leaves at the same level.
How to implement the binary heap
A binary heap is usually implemented with the help of arrays. Any binary tree can be easily stored in the array. Though, because of the binary heap, there will be a complete binary tree which can be stored in a compact way. Though, no spaces are required for the pointers. Each parent or child node is found in the arithmetic or the array indices.
How to implement the pre order traversal in the binary tree with the help of recursion?
For traversing in a non-empty tree in the pre-order criteria, you need to consider three things with every node denoted by n that will start from the root node.
- [N] process
- [L] will recursively traverse to the left subtree
- [R] will recursively traverse towards the right subtree.
What do you know about the binary heap?
A binary tree heap is the one which has the following properties:
- It would be a complete tree where all levels get complete except the last one which has all the possible keys left. This makes them to get supported in the array
- A binary heap can be a min heap or the max heap. In the min heap binary tree, the key root can be minimum among all related keys present. This same property can be recursively true with all the nodes of the binary tree
What do you know about binary search trees
Binary search tree is touted as the data structure which maintains the sorted list of numbers. This is called a binary tree as each of its nodes has a maximum of two children. It is also called as the search tree as it can be used for searching the presence of all numbers in the o [ log n]
What is the DFS algorithm?
DFS algorithm is the one which has the following three variants:
- Preorder traversal: Here, visit the current node before visiting any nodes of the left or right subtree
- Inorder traversal: Visit current node but after visiting all other nodes inside a left subtree
- Postorder traversal: Visit current node but after visiting all other nodes in the left and the right subtree
Name the ways in which you can implement the priority queue
To implement the priority queues, you can take the help of other data structures including:
- Unorder list
- Unordered array
- Ordered list
- Ordered array
- Binary search tree
- Binary heap
- Balanced binary tree
What are the pros and cons of BST?
Various advantages of BST includes:
- If we can implement the binary search tree, we can keep the cost insert(), delete() or lookup()
- BST is considered faster than an array related to searching, insertion and deletion
- Codes in this case are simply relative to that of other data structure
Disadvantages of BST includes:
- BST is generally considered to be slower than the array
- Binary tree structures can be imbalance or degenerated
- We can implement the balanced search tree with the self-balancing techniques
Define AVL tree
AVL trees are touted as the height balancing search trees. This tree checks the height of a right or left subtree to make sure that the difference will not be more than 1. The difference will be called the balanced factor. It allows you to search the elements in the O [log n] inside the elements of a given tree.
The balancing factor of node [ n] in the binary tree will be its height difference. You can do left, right, left right or right left rotations with a given tree.
What do you understand about a balanced tree?
A tree is said to be balanced if the height of its left or right subtree differ by one. If its left subtree is balanced and its right subtree is balanced.
Why do we use binary search trees?
When we implement the binary search tree, we keep in mind the cost of its insert (), lookup(), delete() till O (log n). The benefit here is that the lookups can easily be done in the logarithmic time which is better than the linear time.
Wrapping Up
Tree questions are equally important as array questions, SQL problems, DBMS questions, string questions and any other DSA concept.
With this guide, you can learn about some of the common tree questions that are asked in a coding interview.
