Data Structures is one of the trending topics in the field of software. Hence knowing Data Structure Interview Questions is very necessary. In today’s most high-end software companies, Data Structure Viva Questions are not just based on practical knowledge. Instead, the Interview Questions on Data Structures are Theoretical Interview Questions and basic Algorithms-Based Interview Questions.
Questions on Basic Data Structure Algorithms include the static data structure formats like Arrays and Dynamic Data Structures like Linked Lists, Binary Search Tree, etc. Hence, to be completely prepared for an interview in the software field, it is important to know the nook and corner of Data Structure Interview Questions.
In the hiring process of companies like Amazon, Google and Microsoft, the students are expected to have a keen knowledge and understanding of the key topics of Data Structure Interview Questions, like Linked Lists, Binary Search Trees, Graphs, etc.
Basic Data Structure Viva Questions
1. What are Data Structures?
Data Structures is one of the basic formats in which data can be stored and represented. Data is the representation of facts, concepts, and instructions required for all sorts of computing purposes inside a processor. The various formats in which all of this data can be efficiently organized come under Data structures.
The efficiency of the various data structures is dependent on whether or not the operations on the data can be performed effectively. Data Structures is one of the most basic concepts that every programmer should know to have an idea about the various ways in which data can be arranged for the required set of instructions to be executed.
2. How are Data Structures different from File Structures?
File Structures are mostly end-user data, like word documents, photos, videos, etc. This data is used mainly by the people around rather than by the programmers. All the data that is usually stored in a computer’s hard disk or external storage devices like USB comes under file structures. The data inside file structures are mostly inserted, accessed, or deleted only by the user rather than the processor.
Data Structures, on the other side, are storage structures inside the main memory of the computer, i.e., RAM. This data consists of the programmer’s input like variables and constants that are required for forming instructions that have to be processed by the machine software. Data structures thus are easily accessed by the processor to execute instructions and can be deleted after the compiling and processing part of those instructions is over.
3. Why do we need Data Structures?
All programming languages and every software designed using those programming languages have to deal with a lot of data. The basic concept in any programming language is sending instructions to the machine, and these instructions are designed in algorithmic format. Implementing tons of user input into such an algorithmic format is one of the challenges faced by programmers, and Data Structures are a perfect solution for that.
Data Structures are thus the basic format in which data is efficiently inputted in large amounts into databases, internet indexing services, etc. They provide several ways in which data can be inserted, accessed, edited, or deleted.
4. List some advantages of Data Structures.
- Reduces complexity in data storage
- Helps in effectively storing large amounts of data, like Big Data.
- Efficient data management of either small or large amounts of user input.
- Saves a lot of time during the designing of code by the programmer.
- Availability of multiple data structure types to choose the right one according to our needs.
- All operations on data, like data addition, data deletion, data movement, etc., can be performed very easily when chosen with the right Data Structure.
- Easy entry and access of online server data from anywhere and anytime
- A secure data storage with very little possibility of data mismatch or data loss.
5. Briefly explain the various operations that can be performed on the data stored inside Data Structures.
There are many different types of data structures, and each data structure has its own rules and methods. But some operations are commonly found in all the different types of data structures.
- Inserting of Data – Adding a new integer, string, character, variable, or any other element into the data structure.
- Deleting of Data – Removing one or many elements that are previously entered into the data structure.
- Searching for Data – Locating and selecting any particular variable or element present inside the data structure.
- Traversing a Data Structure – Accessing a few particular ones or all the elements inside a particular data structure.
- Sorting a Data Structure – Arranging the data structure in a particular order, either ascending or descending or alphanumeric.
6. What is Static Data Structure?
This is a frequently asked Data Structure viva question. Static Data Structures are the type of memory storage formats where the size of the data structure is fixed. The size can be any number of bits or bytes, but the exact value of the size has to be declared at the beginning itself.
During the start of a program, the programmer usually declares the static data structure name along with the size. Later, all through the program, elements can be added, removed, interchanged, or accessed at any time, but the size limit declared at the beginning should not be exceeded.
One of the significant examples of Static Data Structures is Arrays. Any array declaration in programming languages like C or Java is of fixed size at the beginning of the program, known as Static Array Declaration.
7. What are Dynamic Data Structures?
Dynamic Data Structures are the type of memory storage formats where the size of the data structure is not fixed and is flexible. The size of a Dynamic Data Structure is adaptable to any kind of changes during the runtime. The size adapts depending on the memory addition and deletion that takes place while executing the code in runtime.
The Dynamic Data Structure is designed in such a way that the size of the data structure can be expanded or could be shrunk at any time depending on how the program demands while execution.
A Linked List is an example of Dynamic Data Structure. The size of any Linked List keeps changing based on how many elements are added or removed in the program.
8. Can you tell the basic difference between Static and Dynamic Data Structures?
This is an important Data Structure viva question. The basic difference between Static Data Structures and Dynamic Data Structures is memory allocation.
Static Data Structures (SDS) have fixed memory allocated. The memory is allocated at the time of data structure declaration and cannot again be changed all through the course of the program.
Whereas the Dynamic Data Structures (DDS) have adaptable memory sizes that can be dynamically grown or shrunk during the program’s runtime.
9. On what basis are Data Structures divided?
There are so many different Data Structures available for usage. Each data structure has a different format that can be used depending on the requirement of the application. All the different data structures fall under 2 main categories, which are Linear Data Structures and Non-Linear Data Structures.
Linear Data Structures – The type where all the elements of the data or memory are arranged one after the other is known as Linear Data Structure. During data insertion in Linear Data Structures, every element is placed sequentially in the memory. Hence, the traversal of the various data elements is done linearly.
Non-Linear Data Structures – Data Structures where the elements of data are not arranged linearly or sequentially are known as Non-Linear Data Structures. The arrangement of elements can be done in various nonlinear formats, which differ for the various Non-Linear Data Structure types.
10. Can you tell the basic differences between linear and non-linear data structures?
| Linear Data Structures | Non-Linear Data Structures |
| All elements of the input data are arranged linearly or sequentially. | All elements of the input data are arranged non-linearly. |
| Each element can access its adjacent 2 elements, i.e., the element’s previous and next elements. | Each element will be able to access one or multiple elements that are connected to it. |
| All elements can be accessed in a single traversal. | Elements can be accessed by traversing one branch at a time. |
| Linear Data Structures have a simple design. | Non-Linear Data Structures have a comparatively complex design. |
| Examples: Stack, Queue, Linked List, etc. | Examples: Tress, Graphs, etc. |
11. Why do we need various types of Data Structures?
Every application has a different requirement for data access. Stack type Data Structures must be used when the requirement is LIFO (Last in First out) format. Queue type Data Structures have to be used when the requirement is FIFO (First in First out) format, and so on. Hence, depending on the programmer’s need for that particular application, we might require different types of Data Structures.
Having such diverse data arranging and data accessing formats would save time, thereby having minimal runtime. Different types of Data Structures would also increase the efficiency of the program to a great level.
12. Briefly explain the various types of Linear Data Structures.
Data Structures where all the elements are arranged linearly or sequentially are known as Linear Data Structures. Some of the commonly used Data Structures are Stacks, Arrays, Linked Lists, and Queues.
Arrays – A linear data structure where the collection and the arrangement of similar data elements are done in consecutive memory locations.
Queues- – A linear data structure that follows the FIFO format for data entry and exit.
Stack – A linear data structure that follows the LIFO format for data entry and exit.
Linked Lists – A linear data structure where instead of consecutive memory locations, each data element has a pointer that links to the next memory element.
13. Briefly explain the various types of Non-Linear Data Structures.
Data Structures where the arrangement of elements can be done in various nonlinear formats instead of arrangement in consecutive memory locations. Some of the commonly used Non-Linear Data Structures are Trees and Graphs.
Trees – A non-linear data structure where data elements are interconnected in a parent node and child node format. The structure starts with a single parent node and expands into multiple nodes through branches, where each node represents a data element.
Graphs – A non-linear data structure that consists of a finite set of vertices and edges which interconnect a pair of nodes, where each node represents a data element.
14. Give some real-life applications of Data Structures.
Stacks – Undo operation in word, going backwards after opening a browser page, photos shown in phone gallery in the exact order they are taken by the camera or downloaded, the order of emails received according to time.
Arrays – a linear collection of questions in an online exam question paper, all applications of matrices, bullet points, or inserting contents in a table in Word.
Linked Lists – previous & next options in all images/photos viewing applications in computer or laptop, previous & next options in online story reading websites, playing songs in the order in a music player, a link for a new page that is mentioned in a current page in browser web pages.
Queues – Order of serving requests when multiple requests are given at a time to the operating system, transfer of data packets in communication protocols, downloading in order when multiple photos are requested to download at a time.
Trees – Level of indexing, i.e., List Level in bullets and numbering in Word, relational databases, the domain name server (DNS).
Linear Data Structures Interview Questions
15. What do you know about 1D Arrays?
This is an important Data Structure Interview Question. An array is a data structure where multiple elements of the same data type can be stored under a single variable name. In a one-dimensional array (1D Array), all the data elements (integers or letters or words) are stored under a single subscript. A one-dimensional array can therefore be considered as a list of similar variables that are stored adjacent to each other in consecutive memory locations.
A 1D can be declared with its variable name at the start of the program. Whenever an element is added into that variable, the positions of the variable from 0 to array size would start getting occupied in ascending order.
A simple syntax for the declaration of a 1D Array is:
Data_Type Var_Name[Data_Element];
16. What do you know about Multi-Dimensional Arrays?
An array is a data structure where multiple elements of the same data type can be stored under a single variable name. A multidimensional array, in particular, can be called an array of arrays. In a multidimensional array, multiple elements can be stored under the same variable while further grouping those elements into multiple subscripts.
The memory allocation in a multi-dimensional array is done in a matrix form. Hence, the multiple subscripts can be considered as multiple columns of the matrix. They are commonly declared with 2 or 3 subscripts, i.e., 2X2 or 2X3 or 3X2 or 3X3. In all these cases, 2 subscripts are chosen, and new data elements can be added to either of the subscripts just by mentioning the variable name and the desired subscript to which the element has to be added.
17. What are the programming languages that make use of Array Data Structure?
This is an important Data Structure Interview Question. Arrays are the most widely used data structures during the logic design of both simple programs and complex programs. The programming languages where arrays are commonly used are C, C++, Java, Python, and C#.
C/C++: Data_ Type Array_ Name[Array_ Size];
Python: Python makes use of arrays through “Lists”, or there is an option to import the “array” module. When the “array” module is imported, the syntax for array declaration is
import array as arr
Array_ Name = arr.array(‘Datatype_ Code’,[Array_ Elements])
(Here, the Data Type _Code is a predefined code that differs for every data type. ‘i’ is for integer, ‘d’ is for decimal, etc.)
Java: Data_ Type Array_ Name[];
C#: [Array_ Size]
18. What are Stacks?
Stacks are one of the most basic linear data structures. The data insertion and removal out of a stack are based on either LIFO format or FILO format. A stack can be considered as the type of data structure where all the data elements are piled upon each other vertically or horizontally. On every data insertion, a new element is piled upon the existing stack of elements.
Stacks have the most common real-time applications, like undo operation in word, going backwards after opening a browser page, photos shown in phone gallery in the exact order they are taken by the camera or downloaded, the order of emails received according to time, etc.
19. Explain briefly about the LIFO format.
LIFO stands for Last in First out. It is one among the several formats that can explain the data elements entry and data elements exit inside a data structure. The LIFO format is widely used for the Stack data structure.
In a stack, every new element inserted is placed on the top of the existing last entered element. And hence, for removal elements, any random element cannot be removed. Only the topmost element on the stack is open for data exit. This is the topmost element is the element that is last entered in the stack. Thus, LIFO. The last element entered into the stack is the first element that can be removed from the stack.
20. Name the basic operations performed on Stacks.
Push – Used when a new element has to be added to the stack. The operation sends a stack overflow error if the size of the stack is full.
Pop – Used when a new element has to be removed from the stack. Sending a stack is an empty error when the pop operation is attempted on an empty stack.
Top – Returns the topmost element on the stack. The top element is the last added element into the stack.
isEmpty – A boolean function that returns True if the stack is empty, i.e., no elements are present in the stack. And returns False if the stack has at least one element in the stack.
21. What are Queues?
The queue is a linear data structure that works similarly to a real-life queue. The data entry and exit of a queue are based on the FIFO format. Elements to a queue are placed consecutively inside the memory in such a way that the memory allocation satisfies the FIFO format.
Queues have some of the most common real-time applications like the order of serving requests when multiple requests are given at a time to the operating system, transfer of data packets in communication protocols, downloading in order when multiple photos are requested to download.
22. Explain briefly about FIFO.
This is a frequently asked Data Structure Interview Question. FIFO stands for First in First out. It is one among the several formats that can explain the data elements entry and data elements exit inside a data structure. The FIFO format is widely used for Queue data structures.
Inside a queue, the first data element entered into the data structure is the first element that can be accessed for removal. No matter how many more elements are present behind, the first element is the only element that can be accessed. Once the first element is removed, then the second element would be the only element that can be accessed, and so on. Hence, the queue data structure is known to follow the First in First out (FIFO) format.
23. What is a Double Ended Queue?
A Double Ended Queue (i.e., Deque) is a hybrid type of the queue data structure. While in a queue, only one end can be used for data entry and the other end for data removal. In Double Ended Queue, both sides of the queue structure can be used for data entry, and both sides can also be used for data removal.
24. What are the various types of Double Ended Queues?
Input Restricted Deque and Output Restricted Deque are 2 variations of a deque.
In an Input Restricted Deque, data elements deletion can be done on both sides, but the data elements insertion is restricted to only one side of the queue.
In an Output Restricted Deque, data elements insertion can be done on both sides, but the data elements deletion is restricted to only one side.
25. Briefly explain about Priority Queue.
A priority queue is a different version of the queue data structure where every data element inserted is assigned a priority number. The priority number of the element inserted decides which elements will first reach the front side of the queue for accessing those elements. Data elements of higher priority are arranged to be at the start of the queue compared with the data elements of the lower priority.
If few data elements have the same priority, then the order of their positions for the placement at the first side of the queue depends on which element is entered. The data element that is entered is considered to be the first element to be accessed, even though there are few other elements with equal priority.
When an element has a priority higher than the priority number of all the existing data elements, then this element is placed first for access, even though the other elements are inserted first. Hence, Priority Queue is a hybrid combination of both LIFO and FIFO formats. LIFO format is used for deciding the order of data elements access based on priority, and FIFO format is used for deciding the order of data elements access among the ones that have the same priority.
26. How does a Circular Queue work?
This is an important Data Structure Interview Question. A circular Queue is a linear data structure where the data entry and data removal are based on FIFO (First in First out) Format. It is similar to an ordinary queue data structure, but in a circular queue, the front position of the queue is connected to the last position, thereby making a queue. This hence removes the concept of which is the first position and which is the last position of the queue.
The main working of circular queues is that whenever the last position of the queue is filled, then the labelling of the first position is shifted to the last position, and the data element can be inserted into the previous first position of the queue making it the last entered position. The concept of circular queue thus solves the problem of having empty memory locations in the starting positions upon data elements removal.
27. What are the various types of Linked Lists?
There are 3 different types of Linked List data structures. The variations are based on how the linked list can be traversed.
- Simple Linked List – Traversing of the list or accessing of data elements can be done in the forward direction only.
- Doubly Linked List − Traversing of the list or accessing of the data elements can be done in the forward direction and backward direction also.
- Circular Linked List – Similar to a circular queue. The last item of the circular linked list contains the link of the first element as next, and the first element has a link to the last element as previous.
28. Explain the pseudo-code for entering a node at the front of a Linked List.
Whenever an element has to be added to the Linked List, a variable by name newNode is created, and the existing final node is linked to that new node. If the front node is null (i.e., Linked List is empty), then the newNode is equated as the last node. If the front node is not null (i.e., Linked List has some existing elements), then the newNode is placed next to the front node.
29. Define Ordered Lists.
Ordered Lists are a type of data structure where multiple data elements can be stored in a list format. Each data element in that list holds a relative position that depends on a particular characteristic of that item which is given by the programmer. All operations on an ordered list are almost similar to that of an unordered list.
Thus, all the elements in an Ordered List are simply dependent on a particular order. The particular order may be an ascending order of elements or descending order of elements. The variation is based on a means of comparison that is specified in the code by the programmer.
30. Do you know anything about Hashing in data structures?
Hash Tables are a type of data structure where the data elements are stored in an associative format. They are similar to multi-dimensional array data structures. Hashing is a technique used in these hash tables data structures.
Hash tables consist of both keys and values for all the inserted data elements. Hashing is a process of mapping these keys and values into a hash table. The mapping is done by using a simple function called the hash function. The basic syntax for creating a linked list based hash table and then hashing keys and values are as follows:
var table = new LinkedList[1000]
hash = key % 1000
table[hash].AddFirst(key, value)
Non-Linear Data Structures Interview Questions
31. What is the structure of Trees?
This is a most frequently asked Data Structure Interview Question. Trees is a non-linear data structure that is arranged in the form of hierarchical data structures. According to this hierarchical format, every tree data structure starts with a single node called the parent node. All the other data elements are present under it in various hierarchies. All these nodes are linked through branches.
Every node is linked to a minimum of zero or a maximum of any number of nodes through branches. To a given node, the nodes that are connected to it from a higher level in the hierarchy through branches are known as parent nodes and the nodes that are connected to it to a lower level in hierarchy though branches are known as child nodes.
32. What are the various terms used for a Tree Data Structure?
Root Node – The topmost node from where the hierarchy starts.
Leaf Node – If there are no child nodes for the given node, it is known as a leaf node.
Internal Nodes – All the nodes that have a parent and at least one child are known as internal nodes.
Parent Node – Nodes that contain sub-nodes, i.e., nodes that are connected to their adjacent lower-level nodes though branches are known as parent nodes.
Child Nodes – If the nodes have an upper hierarchy through which they are connected or originated, then they are known as child nodes.
Sibling Nodes – Nodes that have the same parent node are known as sibling nodes.
Edge – The branches, i.e., the link that connects the parent nodes with child nodes, is known as the edge. For every n number of nodes, there will be n-1 edges.
Depth of Node – Number of edges between the root node and the given node.
Height of Node – Number of edges between the given node and the leaf node.
33. What do you know about the In-Order Traversal of Trees?
In-Order Traversal is a type of Tree Traversal that takes place for accessing or searching the nodes in a tree. For every given node in a tree, there are 0 or more child nodes. For In-order Traversal of Trees, the direction of traversal between the parent nodes and child nodes is the left node, the root node, and the right node.
34. What do you know about the Pre-Order Traversal of Trees?
In-Order Traversal is a type of Tree Traversal that takes place for accessing or searching the nodes in a tree. For every given node in a tree, there are 0 or more child nodes. For In-order Traversal of Trees, the direction of traversal between the parent nodes and child nodes is the root node, left node, and right node.
35. What do you know about the Post-Order Traversal of Trees?
In-Order Traversal is a type of Tree Traversal that takes place for accessing or searching the nodes in a tree. For every given node in a tree, there are 0 or more child nodes. For In-order Traversal of Trees, the direction of traversal between the parent nodes and child nodes is the left node, right node, and the root node.
36. Say a few words about Parsing Notations in Trees.
The parsing notations are equations that represent the traversal of data elements and the addition and deletion elements inside the Tree Data Structures. There are 3 different types of parsing notations:
- Infix: Operators are placed between the operand variables accordingly (example: a+b).
- Prefix: Operators are placed before the operand variables (example: +ab).
- Postfix: Operators are placed after the operand variables (example: ab-).
All the 3 notations have the same meaning, but their usage differs depending on the application.
37. List the different types of Tree Data Structures?
This is an important Data Structure Interview Question.
General Tree – no particular rules on the hierarchy of the nodes of the tree
Binary Tree – every parent node (except leaf nodes) should have 2 child nodes
Binary Search Tree – the value of the left child node of the parent node should always be < or = the parent node, and the value of the right child node of the parent node should always be > or = the parent node.
AVL Tree – a self-balancing binary tree
Red Black Tree – alternate hierarchy of the trees is considered either red or black to balance the tree
N-ary Tree – Trees with a restriction of the number of child nodes for a given parent node to be less than or equal to N.
38. What are Binary Search Trees?
Binary Search Trees are tree data structures whose nodes have to follow a particular restriction. The restriction is that the value of the left child node of the parent node should always be less than or equal to the parent node, and the value of the right child node of the parent node should always be greater than or equal to the parent node.
39. Explain the pseudo code to count the number of nodes in a given Binary Tree.
A variable named ‘node’ of the structure data type is created, and the ‘root’ is a pointer to the node variable. Then, the pointer value is placed inside a variable by the name ‘countnodes’. As long as the pointer points to one of the nodes, the root variable is not null. And as long as the root variable is not null, the pointer is made to shift to the adjacent nodes of the branch. Whenever a shift takes place, counter=counter+1. Thus, at the end, when all the nodes are done by the pointer, the counter value gives us the number of nodes present in the given Binary Tree.
40. What are Heaps? What are the various types of Heaps?
This is a common Data Structure Interview Question. Heaps is a type of tree data structure where each node follows the rules of a binary tree. Here, the value inside all parent nodes should always be either less than or greater than the values stored inside the child nodes of that respective parent node.
There are 2 basic types of Heaps.
- Max-Heaps: Values of the parent nodes are always greater than the child nodes.
- Min-Heaps: Values of the parent nodes are always lesser than the child nodes.
41. What is a Graph Data Structure?
Graph Data Structure is a type of non-linear data structure that consists of a finite set of vertices and edges which interconnect a pair of nodes, where each node represents a data element. Every node is connected to some or all the nodes with the help of vertices.
42. How do Trees differ from Graphs?
The nodes of a tree are connected in a hierarchical manner where a given is only connected to its parent node and child nodes. On the other hand, the nodes of a graph are interconnected through a finite set of vertices that help every node be joined to a few or all the nodes.
43. Do you know anything about an AVL Tree?
AVL is a type of binary search tree and is a particular restriction that makes it a self-balancing tree. The restriction is that the difference between the left and right subtrees of a given parent node cannot be greater than one for all the nodes belonging to the subtree.
Questions on Sorting & Searching Algorithms
44. What are sorting algorithms?
For this Data Structure Interview Question, you could answer; In a data structure, there will be multiple data elements that are entered at various stages during the runtime of the program. A sorting algorithm helps the programmer by sorting all these data elements into a particular order. A logic of comparison is given in the sorting algorithm, based on which all the data elements are sorted and re-arranged.
45. Name some of the most commonly used sorting algorithms.
Some of the commonly used sorting algorithms are:
- Selection Sort
- Insertion Sort
- Quick Sort
- Iterative Quick Sort
- Recursive Bubble Sort
- Bubble Sort
- Merge Sort
- Iterative Merge Sort
- Radix Sort
- Bucket Sort
46. Tell a few lines about Bubble Sort Algorithms?
This is an important Data Structure Interview Question. Bubble sort is a simple sorting algorithm that is used on linear data structures like linked lists or queues. This sorting algorithm compares two adjacent elements and swaps them (sorts them) if they are not in order. It is one of the best ways to sort the data in ascending or descending order. The only disadvantage is, bubble sort algorithm is time taking as only two elements at a time can be compared.
47. What is the use of searching algorithms?
In every data structure, an unlimited number of data elements can be inserted. For complex programs, software design, or web design, there may be thousands of data elements inserted. Finding a particular element among this vast set of data entries is a complex process. Hence, searching algorithms simplify the process for data structure traversal and access to data elements.
48. Name some of the most commonly used searching algorithms.
- Linear Search
- Binary Search
- Ubiquitous Binary Search
- Jump Search
- Interpolation Search
- Exponential Search
- Fibonacci Search
- Sublist Search
49. What do you know about Binary Searching Algorithms?
Binary Search is a type of searching algorithm to search inside a sorted array. The binary search process works by continuously dividing the search interval in half. Initially, all the nodes are divided into half, and the first half is searched. The first half again is divided into half, and the process continues until there is only one node left. If that one node is the required node, then the value is returned; else, the searching process continues with the remaining nodes.
50. Tell me about BFS and DFS of a Tree?
BFS stands for Breadth-First Search Algorithm, and DFS stands for Depth First Search Algorithm. Both these algorithms are 2 ways of traversing inside a tree.
Both in BFS and DFS, all the data elements that have to be traversed are placed inside the queue, and every element that is traversed is removed from the queue. The traversing hence continues as long as there are elements left in the queue. When the queue is empty, the algorithm ends.
For a Breadth-First Search algorithm, the algorithm starts at the root node and continues in the order of the breadth of the node being traversed. The breadth of the node is defined based on the order of the hierarchical level in which the tree is present.
For a Depth First Search algorithm, the algorithm starts at the root node and continues in the order of the depth of the node being traversed. The depth of the node is the number of edges between the root node and the given node. In order, pre-order and post-order traversals come under the depth-first search algorithm.
Conclusion
Various programming applications are prominently dependent on Data Structures. For the good use of Data Structure applications, it is necessary to know the above-mentioned Data Structure Theoretical Questions and Data Structure Interview Questions.
Knowing the difference between Linear Data Structures and Non-Linear Data Structures plays a prominent role to get clarity on the topic. It is also very important to choose the right and the apt Searching Algorithm and Sorting algorithm depending on the particular application and the type of Data Structure that is chosen.
More Resources: Tips to Crack the Campus Placement Interview | How to explain reason for a Job change | Job vacancies in Kuwait | Job vacancies in Jamshedpur
