Data Structures Interview Questions
Data Structure and algorithm questions are important for any programming job interview, especially for java based and Data Science roles. Companies around the world invest millions of dollars in integrating their data handling systems.
Tekslate experts compiled top Data Structure and Algorithm interview questions for freshers and experienced candidates. Our Data Structure Interview Questions will help you to crack the interview and acquire your dream job.
Q1) What is meant by Data Structure?
Ans: The data structure is a way that determines how to arrange and control the data. It describes the characterizes the connection between them. A few instances of Data Structures are Linked List, Stack, array, Queue, etc. Data Structures are the focal pieces of numerous computer science algorithms as they empower the developers to deal with the information effectively.
Q2) Describe different types of Data Structures?
Ans: Data Structures are classified into two types as follows:
- Linear Data Structure
- Non-Linear Data Structure
Q3) What is meant by Linear Data Structure?
Ans: A data structure is called linear if all its components are organised in the consecutive order. In linear data structures, the components are stored in a non-hierarchical level where every item has the predecessor and successor aside from the first and last component.
Q4) What is meant by Non-Linear Data Structure?
Ans: The Non-linear data structure doesn't form a sequence; for example, each component is associated with at least two different things in a non-linear arrangement. The data components are not composed in the consecutive structure.
Q5) List out the area of the applications of Data Structure?
Ans: Data Structures are applied in the following areas of Computer Science:
Database Management System
Statistical analysis package
Q6) List out various Data Structures available?
Ans: Data Structures vary by programming languages and commonly available data structures are array, queues, stack, graphs, tree, and list.
Q7) What do you mean by Abstract data type?
Ans: A data type is a collection of a set of operations and values. Abstract data type refers to the mathematical idea that describes the data type.
It is a valuable tool for indicating the logical properties of a data type.
ADT comprises of two sections
1. Values definition
2. Operation definition
Instance:- The value definition for the ADT RATIONAL refers that RATIONAL value consists of two integers, where second doesn't mean equivalent to zero.
The operation definition for ADT RATIONAL incorporates creation (make level headed) multiplication, addition and test for correspondence.
Q8) List out various operations that can perform on different Data Structures?
Ans: Following are the various operations that can perform on different data Structures:
Insertion: You can add new items to the give collection of data items
Deletion: You can delete or remove the data item from the given collection of data items
Searching: You can find the location of the data items from the given collection of data items.
Traversal: You can access each data item in the given collection of data items.
Sorting: You can arrange the data items either from ascending to descending order.
Q9) Differentiate between storage structure and file structure?
Ans: Following are the key differences between Storage Structure and File Structure:
Data is stored in Ram and Disk
Data is stored on Disk
Customized storage policies
Standard File storage policies
High Compatibility with external applications
Low Compatibility with external applications
Q10) What is meant by linked list in Data Structure?
Ans: A linear data structure or a sequence of data objects where components are collected in contiguous memory areas. The components are connected, utilizing pointers to shape a chain. Every component is a different item, called a node. Every node has two things: a data field and a reference to the next node. The entry point in a connected rundown is known as the head. The rundown is vacant, the head is an invalid reference, and the last hub has an invalid reference.
A linked list is a dynamic information structure, where the number of nodes is not fixed, and the list can develop and shrink on interest.
It is applied in situations where:
- We manage an unknown number of items or don't know the number of things on the list.
- We need consistent time inclusions/cancellations from the rundown, as progressively figuring where time consistency is basic.
- Arbitrary admittance to any components isn't required.
- The calculation requires an information structure where items should be put away independent of their actual memory location.
- We need to embed things in the rundown as in a need line.
- A few usages are stacks and lines, diagrams, index of names, dynamic memory distribution, and performing number juggling procedure on whole long numbers.
Algorithms Interview Questions
Q11) Explain various types of search methods that are available in Data Structure?
Ans: There are two types of search methods that are available in Data Structure: They are as follows:
- Linear Search
- Binary Search
Linear Search: It includes iterating over a data unit to implement the required operations.
Binary search: is more proficient such that it can split the data units into chunks, and then it performs search operations on the given data elements.
Q12) Are linked lists considered as linear or non-linear Data Structures?
Ans: Linked lists are considered non-linear and linear data structures relying on the application. To access the strategies, you can utilize a linear data structure. And if you want to store the data, you can use a non-linear data structure.
Q13) Give detailed information about Binary Search?
Ans: Binary Search a sorted array by partitioning the search interval repeatedly. The key starts checking the interval covering the whole array. If the key value is less than the middle key of the interval, it searches the lower half of the array. In any case, the key value is greater than the middle key of the interval, and then it searches the upper half of the interval. This process is repeated until the key is found in the given data set.
Q14) How is the array different from the Linked list?
Ans: Following are the key points that differentiate the array from the linked list:
Insertion and Deletion
Insertion and deletion of nodes is an easy process, as we update the address of the next pointer of a node. But it is expensive to do in the array to create the room for the new components and move existing components.
Dynamic Data Structure
As a Linked list is a dynamic data structure, there is no compelling reason to give an underlying size as it can develop and contract at runtime by allocating and deallocating memory. In any case, the size is restricted in an array as the number of components is statically stored in the primary memory.
No wastage of memory
As the size of a linked list can decrease or increase it depends upon the program, and memory is assigned just when needed, there is no memory squandered. On account of an exhibit, there is memory wastage. For example, if we declare the array of the size 10 and store just five components in it, then the space for five components is wasted.
Data structures like stack and queues are more handily executed utilizing a linked list than an array.
1. A few situations where we utilize a linked list over array are:
- At the point when we know as far as possible on the number of components ahead of time
- When there are an enormous number of additional or eliminate tasks
- When there is no huge number of irregular admittance to components
- At the point when we need to embed things in the rundown, for example, while actualizing a need line
2. A few situations where we use array over the linked list are:
- At the point when we need to record or haphazardly access components
- When we know the number of components in the exhibit already, we can designate the right measure of memory.
- At the point when we need speed while emphasizing through all the components in the grouping
- When memory is a worry; filled exhibits utilize less memory than connected records, as every component in the cluster is the information however each connected rundown hub requires the information just as at least one pointer to different components in the Linked list.
In outline, we think about the prerequisites of time, space, and simplicity of usage to conclude whether to utilize a linked list or array.
Q15) List out various types of Linked list in the Data Structures?
Ans: Following are the various types of linked list in the Data Structure:
Single Linked List: In this type of linked list, every node store the reference or address of the next node in the given list, and last node stores the next address or null values for instance 1-> 2.>3->4->5->Null
Doubly Linked List: In this type of linked list, there are two references associated with each node. One reference point to the next node and another point refers to the previous node. Instance: 1<->2<->3<->4<->Null value.
Circular Linked List: In this type of linked list, all the nodes connect to form a circle. There will be no null value in the circular linked list. Even it is a single circular or double circular linked list.
Q16) Explain about doubly-linked lists and give an example?
Ans: It is a typical type (double-linked list LL) of a linked list where a node has two connections: interfaces with the following hub in the arrangement and another that associates with the previous node. This permits crossing across the information components in the two ways.
Following are the instances:
- A music playlist with next and previous navigation buttons.
- The program is reserved with BACK-FORWARD visited pages.
- The redo and undo functionalities on a portal where you can switch the node to get to the last page
Q17) Give detailed information about the binary tree in a data structure?
Ans: A binary tree is a tree data structure with two nodes. In the binary tree, one root node and two nodes on the root node's left and right side. A binary tree is considered an extended linked list.
Example: Binary Tree
Q18) What is an algorithm?
Ans: An algorithm is a step by step technique for solving an issue or controlling data. It characterizes a set of guidelines to be executed in a specific order to get the desired output.
Q19) Why do you need to do an algorithm analysis?
Ans: A problem can be addressed in more than one way using many algorithms. Algorithm analysis assesses the necessary assets of a calculation to take care of a particular computational issue. The measure of reality assets needed to execute is likewise decided.
The time intricacy of an algorithm quantifies the measure of time taken for an analysis to run as a component of the information's length. The space complexity evaluates the measure of memory or space taken by an algorithm, to run as an element.
Q20) What is meant by stack in the Data Structure?
Ans: A stack is an abstract data type that indicates a linear data structure, as in an actual stack or heaps where you can take the top items off the stack to eliminate things. Subsequently, insertion(push) and deletion(pop) of items happen just toward one side called the top of the stack, with a specific request: LIFO (Last In First Out) or FILO (First In Last Out).
Q21) Where can you use stacks?
Ans: Evaluation, Expression, conversion of evaluating infix, postfix, and prefix expressions
Q22) Explain about Postfix Expression in the Data Structure?
Ans: A postfix expression is a collection of operands and operators, where the operators are placed after the operands. The postfix expression will be as follows in the data structure.
In the above expression, abc are operands, and + & - are the operators.
Q23) How to evaluate postfix expression using a stack data structure?
Ans: To evaluate postfix expression using a stack data structure using the following steps:
It reads all the operands and operators in the postfix expression one by one from left to right.
Next, if the reading symbol is operand (a,b,c), it pushes the operand on to the stack.
If the reading symbol is operator (+, -, *), then it performs pop operations on operands and the result is pushed onto the stack.
Finally, it performs a pop operation and displays the popped value as the result.
Example: 5 3+ 8 2 -*
- The above postfix expression it evaluates in the following way:
- It reads the expression from the left so 5, 3. So here 5 and 3 are pushed onto the stack.
- Next, it reads the operator so it performs pop operations on the operands and puts the result of the operands on to the stack.
- So here the result of 5+3=8 will be pushed onto the stack.
- Again it starts reads the expression, it pushes 8, 2 onto to the stack, and now it reads the operator (-) then it performs pop operation on the operands in the stack
- Operands in the stack are 8, 8, 2. So now pop operation performs on 8, 2 and the result of the two operands is (8-2)=6
- Now the result 6 is pushed onto the stack. And again it reads the expression.
- It reads the operator * in the expression. It will perform the pop operation on the operands which are on the stack. Then 8*6= 48. And then finally it displays the result.
Q24) Explain about infix expression in the data structure?
Ans: A infix expression is a collection of operands and operators, where the operators are placed between the operands. The infix expression will be as follows in the data structure.
Expression: (a+b) * (c-d)
In the above expression a, b, c, d are operands and +, -, * are operators.
Q25) Explain about prefix expression in the Data Structure?
Ans: A prefix expression is a collection of operands and operators, where the operators are placed after the operands. The prefix expression will be as follows in the data structure.
In the above expression a, b are the operands and + is the operator.
Data Structures Interview Questions For Experienced
Q26) Explain about push and pop operation in the Data Structure?
Ans: Both push and pop operations are used to insert and remove or delete the data from the data structure stack. The push operation indicates that users can insert or add the data into the stack. And the pop operation is used by the users to remove or delete the data from the stack. Generally, the top-most data item is considered when users perform push and pop operations on the stack.
Q27) Explain about Queue in the Data Structure?
Ans: A queue is an abstract data type that defines an ordered list and linear data structure, using the First In First Out (FIFO) operation to access data items. Delete operation can be performed only at the other end called FRONT and the Insert operation can be performed only at one end called REAR.
Q28) List out some applications of the queue in the data structure?
Ans: To organize jobs as in the accompanying situations:
- A waiting list for shared resources like CPU, printer, image uploads, and call centre systems. It will process according to the FIFO (First in First out)
- In the asynchronous transit of data, for instance, sockets, file IO, and pipe.
- As buffers in applications like CD players and MP3 media players
- To keep up the playlist in media players (to add or eliminate the tunes)
Q29) What is meant by dequeue in the Data Structure?
Ans: dequeue means double-ended queue, where the data items can insert or delete at both ends called Rear or Front.
Q30) List out the operations that can perform on Queues?
Ans: The following operation can perform on Queue:
dequeue(): it deletes or removes the data items from the queue.
enqueue(): It inserts or adds the data items to the queue.
Init(): this operation is used for initializing the queue.
IsEmpty(): this operation is used to check whether the queue is empty or not.
front(): this operation is used to get the value of the queue's data item.
rear(): this operation is used to remove the value of the queue's data item.
Q31) Explain the working of LIFO in the Data Structure?
Ans: LIFO means Last In First Out it is used for Stack in the Data Structure. LIFO is processing data where the last entered data item in the stack will be removed first from the stack—for instance, stacking a deck of cards by placing one card on top of the other card. If we start removing the cards from the stacking, we will first remove the topmost card. This process is called LIFO.
Q32) Explain the working of FIFO in the Data Structure?
Ans: FIFO means First In First Out, which is used for queues in the data Structure. FIFO is processing the data where the first data item is processed first, then newly entered data item processed later. Consider a ticket counter: The person who entered first into the line(queue) will take the ticket first, and then the second person.
Q33) What are the advantages of the heap over the Stack in the Data Structure?
Ans: Generally, both stack and heap are essential for memory and utilized in Java for various necessities:
- Heap is more adaptable than the stack since memory space can be powerfully designated and de-apportioned varying
- Heap memory is utilized to store objects in Java, though stack memory is utilized to store nearby factors and capacity calls.
- Items made in a heap are noticeable to all strings, while factors put away in stacks are simply obvious to the proprietor as a private memory.
- When utilizing recursion, the size of storage memory is more though it rapidly fills-up stack memory.
Q34) What is the sorting algorithm in Data Structure?
Ans: Sorting means arranging the information in a specific organization. Sorting algorithm indicates the best approach to orchestrate information in a specific request. Most basic requests are in numerical or lexicographical requests.
The significance of sorting lies in how information looking can be advanced to an elevated level if the information is put away in a sorted way. Sorting is likewise used to speak to information in more coherent configurations. Following are a portion of the instances of sorting, in actuality, situations.
Telephone Directory: The phone registry stores the phone quantities of individuals sorted by their names so that the names can be looked for without any problem.
Dictionary: The word reference stores words in a sequential request so that looking at any word becomes simple.
Q35) Which sorting algorithm is considered fastest in the Data Structure? Why?
Ans: A single sorting algorithm can't be viewed as best, as every algorithm is intended for a specific information structure and informational index. Be that as it may, the QuickSort algorithm is by and large considered the quickest because it has the best exhibition for most information sources.
Its points of interest over other sorting algorithms incorporate the accompanying:
Cache-efficient: It straightly checks and directly parcels the info. This implies we can capitalize on each reserve load.
Can skip some swaps: As QuickSort is somewhat touchy to include organized appropriately, it can avoid a few trades.
Proficient even in most pessimistic scenario input sets, as the request is by and mostly arbitrary.
Simple adaptation to as of now or generally arranged data sources.
At the point when speed takes need over steadiness.
Q36) List out top sorting algorithms in the data structure?
Ans: Following are the top sorting algorithms in the Data Structure:
- Insertion Sort
- Merge Sort
- Quick Sort
- Bubble Sort
- Heap Sort
- Selection Sort
Q37) What is merge sort and how does it work?
Ans: Merge sort is a divide and conquers algorithm for sorting the information. It works by sorting and merging nearby information to make greater arranged records, consolidated recursively to frame considerably greater sorted records until you have one single sort list.
Q38) What is selection sort and how does it work?
Ans: Selection sort works by more than once picking the most modest number in rising requests from the rundown and setting it first. This cycle is continued advancing close to the furthest limit of the rundown or arranged subarray.
Scan all things and locate the littlest. Switch over the situation as the principal thing. Repeat the selection sort on the excess N-1 things. We generally repeat forward (I from 0 to N-1) and trade with the littlest component (consistently I).
Time Complexity: best case O(n2); worst O(n2)
Space complexity: worst O(1)
Q39) Define the graph in the data Structure?
Ans: It is a non-linear data structure consisting of vertices or nodes connected by edges or arcs to enable storage or retrieval of data. Edges may be directed or undirected.
Q40) What are the applications of graphs in Data Structure?
Ans: Transport frameworks where stations are spoken to as vertices and courses as the edges of the chart
Utility diagrams of intensity or water, where vertices are association focuses and edge the wires or lines interfacing them.
Interpersonal organization diagrams to decide the progression of data and hotspots (edges and vertices)
Neural organizations where vertices speak to neurons and edge the neurotransmitters between them.
Q41) Define the tree data structure?
Ans: The Tree is a recursive information structure containing at least one data node where one node is assigned as the tree's base while the excess nodes are called the root's offspring. The nodes other than the root node are apportioned into the nonempty sets where every last one is called sub-tree.
Q42) List out various types of trees in Data Structure?
Ans: Following are various types of trees in the Data Structure:
- General tree
- Binary tree
- Expression tree
- Tournament tree
- Binary search tree
Q43) What is meant by the AVL tree in Data Structure?
Ans: An AVL tree is a kind of binary tree that is consistently in a condition of partially balanced. The balance is estimated as a distinction between the heights of the subtrees from the root. This self-balancing tree was known to be the first information structure to be planned.
Q44) What are the properties of B Tree in Data Structure?
Ans: A B tree of order n contains all the properties of an N way tree. Furthermore, it includes the accompanying properties.
- Each node in a B-Tree contains all things considered n children.
- Each node in a B-Tree aside from the root node and the leaf node contain in any event n/2 children.
- The root node should have in any event two nodes.
- All leaf nodes should be at a similar level.
Q45) What are the differences between the B tree and B+ tree?
Ans: Following are the key differences between B tree and B+ tree:
Search keys cannot regularly be stored.
Excessive search keys can be present.
Data can be stored in internal nodes as well as leaf nodes.
Data only stored on the leaf nodes.
Searching the information is a more slow cycle since data can be found on leaf nodes and the internal nodes.
Searching is nearly quicker as information must be found on the leaf nodes.
Deletion of internal nodes is so time-consuming and complicated.
Deletion will never be a complex method since elements will always be deleted from the leaf nodes.