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Table of contents
1.
Introduction
2.
What is Data Structure
2.1.
Types of Data Structures
2.2.
Real-world Application of Data Structures
3.
Important Interview Questions
4.
FAQs
4.1.
What Are the Benefits of Learning Data Structures?
4.2.
What Is the Difference Between Push and Pop Operations?
4.3.
What Is a Hash Table in Data Structures?
4.4.
What Is the Time Complexity of Basic Operations Get() and Put() in Hashmap Class?
5.
Conclusion
Last Updated: Mar 27, 2024
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Data Structure Interview Questions| Part 1

Author Aditya Anand
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Anubhav Sinha
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12 Jun, 2024 @ 01:30 PM

Introduction

Welcome Readers! Data Structure is the most basic and important part of Computer Programming as well as Software interviews. No doubt this is a highly required skill in all software interviews. This series of 3 blogs will cover the most important data structure questions. After reading the blog, make sure to visit other blogs by visiting part2 and part3.

We hope you learn a lot from this blog. 

What is Data Structure

A Data structure is a useful tool for both organising and manipulating data. Simply put, it enables data to be used more effectively. There are numerous data structures, each of which is appropriate for a specific set of applications.

It's a basic idea in any programming language, and it's crucial for algorithmic design.

Data Structure is referred to the way data is arranged and modified. It aims to improve the efficiency of data access. When it comes to the data structure, we don't just look at one piece of data; we look at multiple sets of data and how they might be linked together in a logical way.

Recommended Topic hash function in data structure

Types of Data Structures

There are two types of data structures:

  1. Linear data structure: A linear data structure is one in which the data structure's elements form a sequence or a linear list. Examples include arrays, linked lists, stacks, queues, and other data structures.
  2. Non-linear data structure: A non-linear data structure is one in which the elements of the data structure cause nodes to be traversed in sequential order. Examples include trees, graphs, and other similar structures.

 

Real-world Application of Data Structures

Data structures form the core and the most basic foundation of software programming, like any efficient algorithm for a given problem is dependent on how effectively data is structured.
Data Structure

Applications-

Array: Mobile phone contacts
Linked List: Next feature of Music Player
Trees: Indexing in databases
Stacks: Undo and Redo tasks in editors
Queues: In Operating Systems for FCFS scheduling
Graphs: Google Maps, Facebook, and LinkedIn

Some of the other most critical areas where data structures are used are as follows:
    → Artificial intelligence
    → Compiler design
    → Machine learning
    →Database design and management
    →Blockchain
    →Numerical and Statistical analysis
    →Operating system development
    →Image & Speech Processing
    →Cryptography
    →Numerical analysis
    →Operating system
    →Artificial Intelligence
    →Compiler design 
    →Database management
    →Graphics
    →Statistical analysis 
    →Genetics
    →Simulation

Recommended reading: Html interview questions

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Important Interview Questions

  1. What is linear data structure?
    The data pieces in a linear data structure are ordered in consecutive order. The next memory address can be used to find the next time. It is organised and accessed in a logical order. Linear data structures include arrays and lists.

     
  2. Explain the difference between file structure and storage structure?
    → File Structure: Data is stored on a hard drive or an external device (such as a USB) until it is manually removed. A file structure is a representation of data into secondary or  Auxiliary memory.
    Storage Structure: Data (variables, constants, and so on) are kept in the main memory, i.e. RAM, and then destroyed once the function that utilises the data is finished.
     
  3. What is a linked list?
    A linked list is a data structure that consists of a series of nodes, each of which is linked to the next node via a reference pointer. The elements are not stored in memory regions that are close together. A chain is formed by connecting them with pointers.
    Each node has two fields:
    → Data field: For storing data values.
    → Reference field: For storing address.

    Types of linked lists:
    a. Singly Linked List: Every node in this type of linked list contains the address or reference of the next node in the list, with the last node's address or reference set to NULL. As an example, 1->2->3->4-> NULL
    b. Doubly Linked List: There are two references connected with each node: one links to the next node and the other to the prior node. For example, NULL. <-1<->2<->3-> NULL
    c. Circular Linked List: A circular linked list is one in which all of the nodes are connected in a circle. There is no NULL at the conclusion of the string. There are two types of circular linked lists: singly circular linked lists and doubly circular linked lists. For example, 1->2->3->, The last node's next pointer points to the first.
     
  4.  What is a stack?
    Stack is an Abstract Data Type (ADT) in data structures that are used to store and retrieve values in the Last In First Out function.

    (Source: Holycoders)
     
  5. Why do we use stacks?
    Stacks use the LIFO algorithm, which means that adding and retrieving data items takes just O(n) time. When we need to access data in reverse order of arrival, we employ stacks.
    Some notable applications of a stack are:
    → recursive function calls
    → Reverse a string
    → a depth-first traversal of graphs 
    → expression parsing
    → Evaluation of different expressions
    → Check for balanced parentheses in an expression
     
  6. What operations can be performed on stacks?
    Following are the operations that can be performed on a stack:
    → isempty() − checks if stack is empty
    → peek() − gives the value of a front item without removing it
    → isfull() − checks if stack is full
    → enqueue() − adds an item to the rear of the queue
    → dequeue() − removes the item from the front of the queue
     
  7. What is linear searching?
    The linear search aims to locate a specific item inside a data type that is organised in a specific order. The array or list of sequentially organised data objects is available at incrementing memory locations. Linear search compares each data item in a list or array to the expected data item. The temporal complexity of the linear search is O(n) in the average case and O(n) in the worst situation (n2). There is no need to sort the data in the target arrays/lists.
     
  8. What is a graph?
    A graph is a visual depiction of a collection of things in which some objects are linked together by links. The points that connect the interconnected items are called vertices, and the ties that connect the vertices are called edges.
    The graph has the following applications:
    In-circuit networks, vertices are shown as points of connection, and component cables are drawn as edges of the graph.
    → In transportation networks, stations are shown as vertices while routes are drawn as edges of the graph.
    → Cities/states/regions are drawn as vertices on maps, while adjacency relations are drawn as edges.
    → Procedures or modules are treated as vertices in programme flow analysis, and calls to these procedures are shown as edges of the graph.

     
  9. How do depth-first and breadth-firs traversal work?  
    The Depth First Search algorithm (DFS Algorithm) traverses a graph in a depth ward motion and uses a stack to remember to retrieve the next vertex to start a search when a dead end occurs in any iteration.
    The Breadth-First Search algorithm (BFS) traverses a graph in a breadth-wards fashion and uses a queue to remember to fetch the next vertex to start a search when a dead end occurs in any iteration.

    (Source:   logicmojo

    Must Read Algorithm Types
     
  10. What is a tree?
    A tree is a graph with no loops or circuits that are minimally connected.
    A tree is a recursive, non-linear data structure made up of a set of one or more data nodes, with one node serving as the root and the others serving as the root's progeny.

    Some of the applications of trees are:
    Filesystems: files inside folders that are in turn inside other folders.
    → Symbol Table construction
    → Hierarchal data model
    → The manipulation of Arithmetic expression
    → Syntax analysis
    → comments, replies to comments, replies to replies etc form a tree representation.


     
  11. What is a binary tree?
    A binary tree has the unique property that each node can only have two offspring.
    In a binary tree of height k, the maximum number of nodes is 2^k, where k is the height of the tree.

     
  12. What is a binary search tree?
    A binary search tree is a binary tree with a particular requirement that a node's left child has a value less than its parent's value and the node's right child has a value greater than its parent's value.
    Also see, Kotlin Interview Questions
     
  13. What is tree traversal?
    Tree traversal is the process of visiting all of a tree's nodes. We always begin at the root (head) node since all nodes are connected by edges (links).

    There are three ways which we use to traverse a tree −
     a. In-order Traversal
    Call Inorder(root.left) i.e Traverse the left subtree
    Print(root.val) i.e Visit the root
    Call Inorder(root.right) i.e Traverse the right subtree

    (Source:   logicmojo

    b. Pre-order Traversal
    Visit the root, i.e. print(root.val)
    Call Preorder(root.left) i.e Traverse the left subtree
    Call Preorder(root.right) i.e Traverse the right subtree

    (Source:   logicmojo

    c. Post-order Traversal
    Call Postorder(root.left) i.e Traverse the left subtree
    Call Postorder(root.right) i.e Traverse the right subtree
    Print(root.val) i.e Visit the root

    (Source:   logicmojo
     
  14. What is an AVL Tree?
    Height-balancing binary search trees are known as AVL trees. The height of the left and right sub-trees are checked by the AVL tree, which ensures that the difference is less than one. The difference is referred to as the Balance Factor.
     
  15. Explain LRU cache? Also, mention the data structures used in LRU cache implementation?
    This is a common interview question in which the interviewer expects a thorough explanation of the functioning principle. Make an effort to explain it step-by-step.
    The most frequently used data is temporarily stored in cache memory on computers. Because the retrieval procedure is so quick, it's a wonderful approach to getting the most frequently utilised data.
    However, because cache memory is limited, there must be a means to govern which data must be removed from the cache in order to make room for new data. This is when the LRU cache comes into play.

    It's a cache replacement algorithm that clears out the data that hasn't been used in a while to make room for fresh data.
    In order to achieve this, two data structures are used:

    a. Queue: A doubly-linked list is used to do this. The cache size, i.e. the total number of available frames, determines the queue's maximum size. The pages that have been used the least lately will be near the front of the queue, while the pages that have been used the most recently will be near the back.
    b. Hashmap: The page number is the key, and the address of the matching queue node is the value, in a hashmap.

    Head over to This link to practice LRU Cache Implementation.
     
  16. Can you store a duplicate key in Hashmap?
    No, HashMap does not allow duplicate keys. In an entry pair, the key is a unique reference to the Value, as well as the pair's location in the HashMap. When an effort is made to place an entry with an existing key, the old entry will be replaced with the new one. When this happens, HashMap's 'put()' method returns the previously deleted entry.

    Duplicate keys are not permitted in HashMap, although duplicate values are permitted. This means that the same value can be assigned to numerous keys.
    HashMap also allows for a null key, but because this is a key, each HashMap can only have one null key. Multiple null values are possible.
     
  17. Explain the key differences between the B+ tree and the B tree?
    B-trees are a sort of self-balancing tree structure used to store and retrieve large quantities of data quickly.
    → They are frequently confused with the Binary Search Tree, which is a close relative. The Binary Search Tree is a specific form of B-tree, despite the fact that they're both m-way search trees.
    → Multiple key/value pairs can be found in a node of a B-tree, which is sorted ascendingly by key order. This key/value pair is referred to as a payload.
    → When it comes to insert, remove, and search operations, B-trees are seen to be particularly useful because they give logarithmic O(log(n)) time complexity.
    → In a doubly-linked list, all leaf nodes are linked together. Only the leaf nodes store satellite data. Internal nodes solely serve as routers to the correct leaf node and store keys.
    → Data put on the leaf node improves the search efficiency in B+trees because we can store more keys in internal nodes, requiring us to access fewer nodes.
    → Because we just need to delete data from leaf nodes, deleting data from a B+tree is easier and faster.
    → In fact, B+trees are used for indexing in 99 per cent of database management systems.

     
  18. What is the advantage of the heap over a stack?
    When answering these data structure interview questions, attempt to include as many benefits as feasible, along with illustrations if possible. It will demonstrate your domain knowledge to the interviewer. Both heap and stack are components of memory in Java and are utilised for distinct purposes. 

    → Because memory space can be dynamically allocated and de-allocated as needed, the heap is more flexible than the stack.
    → Objects are stored in heap memory in Java, while local variables and function calls are stored in stack memory.
    → Variables kept on stacks are solely visible to the user as private memory, but objects produced in the heap are visible to all threads.
    → The size of heap memory increases when utilising recursion, whereas stack memory quickly fills up.
     
  19. What is the meaning of the stack overflow condition?
    The stack overflow condition occurs when a stack is totally filled and we attempt to put new components onto the stack. There are no more components that can be placed because top=maxsize-1.

     
  20. What is a Balanced Tree and why is that important?
    If the left and right subtrees of any node are the same height, the tree is perfectly balanced. A tree is also said to be height-balanced if the heights of each node's left and right subtrees deviate by no more than one unit.

     
  21. What are the Data Structures that are used to represent graphs?
    The following Data Structures are used to represent the graph:

    → Adjacency set
    → Adjacency matrix 
    → Adjacency list 
     
  22. Explain the method to check if a given Binary Tree is BST or not?
    If a binary tree's inorder traversal is sorted, the binary tree is BST. The goal is to execute inorder traversal and retain track of the previous key value while doing so. Continue if the current key value is greater; else, return false. 

     
  23. What is a doubly-linked list? Give some examples.
    It's a special sort of linked list (double-ended LL) in which each node has two links, one to the next node in the sequence and the other to the previous node. This permits traversal in both directions across the data pieces. 

    Examples of DLL: 
    → The browser's undo and redo functions let you go back to the previous page by reversing the node.
    → Next and previous navigation buttons in a music playlist.
    → BACK-FORWARD visited pages in the browser cache   


Check out this problem - Largest BST In Binary Tree

Must Read Ruby on Rails Interview Questions, Application of Linked List

Read about Application of Queue in Data Structure here.

FAQs

What Are the Benefits of Learning Data Structures?

Data structures can be used to unlock the power of data. You'd have a lot of data that you couldn't simply access if you didn't have data structures. You can retrieve and alter data using data structures to uncover insights.

What Is the Difference Between Push and Pop Operations?

Stack operations such as push and pop are performed. An element is added to a stack when a push action is performed. Pop operations, on the other hand, remove pieces from the stack.

What Is a Hash Table in Data Structures?

A hash table is a data structure that stores data along with an index, or location in memory, for each element. Hash tables are similar to arrays in that data values are kept alongside information about their placement in memory.

What Is the Time Complexity of Basic Operations Get() and Put() in Hashmap Class?

In Hashmaps, the get and put operations have an O(1) time complexity if the key-value pairs are evenly spread across the array of buckets.

Conclusion

In this article, we have extensively discussed Data Structure Interview Questions.

  • We have learned about data structures, types and their applications in the real world. 
  • We have learned important data structure interview questions.

We hope that this blog has helped you enhance your knowledge regarding Data Structure Interview Questions, and if you would like to learn more, check out our articles on Linux interview questions part1Linux interview questions part2AWS interview questions, Excel Interview Questions, and Selenium interview questions. fYou can also consider our Online Coding Courses such as the DSA in PythonC++ DSA CourseDSA in Java Course to give your career an edge over others.

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