# Top 50 Data Structure Questions

## here we discuss the most rated questions that are priorities in interviews and daily coding life

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Hello Everyone, in this article we will discuss Data structure. Data Structure is the most important thing in coding life, as its backbone.

most of the time we skip this and just go with the flow, but please be noted my point.

*without knowing the right path you can’t achieve your goal.*

It's a very large thing if we discuss all things, so here I just point out some main things that we should know.

its highest probability to this points discusses in an interview like Google, Amazon, Microsoft, etc.

if you are a beginner, intermediate, or pro. you all can relate to those points

So The **First** question is What is **Data Structure**.

The majority of enterprise applications store or retrieve data based on different types of data structures. **Data structures** are ways to store data programmatically so that they can be used efficiently.

Arrays, lists, stacks, queues, and many more types of data structures can be used to organize data in memory.

Data structures are not programming languages such as C, C++, Java, etc. Any programming language can utilize these algorithms to organize data.

**The second** question is What’s the difference between **Data Structure** and **File Structure. **because both ways we **store data**.

Data structures are structures that exist on local storage, such as hard drives, while file structures are structures that exist on a hard drive.

The file storage policies, for example, specify how data is to be stored. That is, with regard to file structure, we have standard file storage policies according to the operating system and how the data is to be stored.

If we work with data structures, there are customized policies that facilitate their read-write capability, so that data can be accessed more quickly.

**The third** question is What is **Linked List**.

In interviews or discussions of data structures, this is the most frequently asked question,

Linked lists are a sequence of items connected by links, with each link pointing to an item in the previous list. These lists represent the second most used type of data structure after arrays.

To understand the concept of linked lists, you should familiarize yourself with the following terms.

Data structures consist of nodes, which are individual entities.

Nodes have the capacity to connect other nodes and create a chain.

The continuous chain structure forms to become a linked list.

The most widely used data structure, the linked list, is a very nice method for storing and handling data, where every element in the list is referred to as a node. The most widely used data structure, the linked list, is a very nice method for storing and handling data, where every element in the list is referred to as a node. each node has data present to it and certain addresses associated with it. You can basically use this address to access the data, and of course, the data can be changed in whatever way you want, linked lists come in many types. Links can be created in different ways, such as single linked list, double linked list, circular linked list, etc.

Simple Example Link:- Introduction to Linked List

**The fourth** question is Where are Data structures **Primarily used**.

Therefore, you can say that data structures are everywhere around us. When it comes to information technology, the data structure is integral to almost everything.

The foundational concepts like numerical computation, operating system design, artificial intelligence, compiler design, database handling, and graphics processing, as well as statistics data structures are very necessary today for handling data and finding the most efficient and easiest way to handle it.

see at the end of the day you’re converting raw data into useful information.

In order to find the most effective way to accomplish that, data structures stand out as the most effective choice. So please quote at least four to five concepts that use data structures and explain what they are used for, for example, if you are talking about data structures in database handling, please don’t just say database handling but just elaborate on just one line where you are stating that they are used to form entities such as linked lists they can be stacks and queues, and these queues and stacks provide organized access to data, so make sure

**Five** Questions what are the types of **searching** methodologies used in data structures.

well well, there are two main types of searching that you come to whenever you talk about data structures it’s **linear search** and **binary search.**

**linear search **as the name suggests is basically moving across the data in a linear fashion basically means one after the other in a certain order.

when you talk about **binary search** is a better form of searching because it’s more efficient number one number two is that it splits the data in the middle and then it just forms two entities where it can search on the left side of that and on the right side of the data if the number is lesser or greater respectively more.

on binary search in the next couple of questions but then at this point of time, you need to know that there are two types of searching linear and binary where linear is a simple form of searching in a set order either from left to right or right or left and binary search involves breaking the data into smaller chunks searching in that break it again if required and search and this can be performed any number of times.

**The sixth **Question is how **binary search works.**

Binary Search is a type of searching algorithm. A searching algorithm means you find an item with a particular value in a sorted sequence. Binary search is a type of searching algorithm which finds an item by repeatedly halving the search space.

yes this would be a very important question which has a very very high probability of being asked well binary search as the name suggests it’s two entities the first thing you have to always remember when you work with binary search is that it’ll only work on sorted data well your data either has to be sorted in the ascending order or the descending order compulsorily and once you have the ordered data basically you have an array let’s say you have an array and basically if you have to search an element in this particular array the first thing you’ll do is you will find the middle element of it if it’s an even number your middle number will be considered as the average of those two numbers but then if you have an odd number you will find one singular middle element so then the data is basically split into the left side and the right side now if you arranged it in in the ascending order let’s say we have top seven elements one two three four five six seven four will be the middle element right so if you’re searching for three then of course you already know that you don’t have to search up and above four because it won’t be there your data is already in the ascending order so you’ll of course search below four then again you will split it into one two three four where one and two can be put into one side three and four can be put into the other side and search so this goes on in an iterative order it’s basically all dependent on the order of arrangement and what value you are searching for binary search is really simple to implement considering that you know how the process of iteration works and how it can be put into effective use.

The binary search works in the following manner:

- The search process initiates by locating the middle element of the sorted array of data
- After that, the key value is compared with the element
- If the key value is smaller than the middle element, then searches analyze the upper values to the middle element for comparison and matching
- In case the key value is greater than the middle element then searches analyze the lower values to the middle element for comparison and matching

Simple Example link:- *How Binary Search Works** or*What is binary search | Binary Search Algorithm with example | Data structures

**The seven-question** is how are individual elements accessed in an **array.**

Whenever you talk about arrays there is concept called as indexing indexing is basically denoting the position of elements in an array and whenever you talk about arrays and in majority of the programming languages out there the first position will always begin with zero rather than one so if you have an array size of say 10 elements the last element will not be 10 it will be 9 because n if n is the number of elements in the array then the last element is n minus 1 it is n minus 1 because we are starting from 0 and not from n and then whenever you talk about indexing you can access any element that you desire from this array at any point of time if you want the sixth element let’s say you have an array which is the of the name a and you want the fifth element to it if you want the fifth element of array a you’ll be accessing a of four a of four because a of zero is the first one a of one is the second one a of two is the third one and all the way until you know a of four which becomes the fifth element basically so yes individual elements can be accessed.

when you talk about multi-dimensional arrays multi-dimensional arrays are basically spanned across rows and columns and more so a row entity will have a separate index a column entity will have a separate index so when you have these two index indices mapped side by side together with an of 2 comma 3 or a, of course, it’ll be in brackets a of brackets 2 big brackets 3 this will denote a specific element say in that particular column and in the particular row in discussion as well so I hope I was clear with that and you understand my view.

Simple Example link:- Accessing Array Elements

**Eight** questions are what is a Q**ueue** in **data structures**.

As a matter of fact, queues are very common data structures in the real world, so the data structure entity such as Queue was designed to represent this real-life fact, and when we talk about queues as data structures, we need to understand that it is all about adding elements to arrays in order or removing them so the order is everything in queues, since they are a literal queue.

let’s say whenever you go to the shopping mall or to pick up anything you get in from one direction you pick up something there are people behind you and you get away from the front of the queue right so basically that it’s first come it’s basically first come first served if you’re first in the queue you are served and you are sent off if you’re last in the queue then you have to work your way towards the front end and basically get your order done right so that’s exactly how Q**ueue** work here as well of course whenever we talk about Q**ueue** in literal there are elements such as the top element there’s the front element where you know the data exits the top element is where the data comes in and much more, of course, you can choose to write a tiny piece of code which will denote an array and how a queue can be implemented.

Simple Example link:- What is Queue

**Nine** questions are what is a **Binary tree**,

As the name suggests it’s basically a tree-like structure that has one primary node and then this primary node or the main node or it’s called a root node as well so this root node is split into two nodes and one is on the left and one is on the right hence it’s binary every single element here will have entities which are on the left and on the right and every single node can have only two other nodes as well and these two nodes, of course, can have two more nodes and it will go on exponentially but then a binary tree will consist of two nodes left and right to the root node and of course if you’re wondering where binary trees are used or if you’re wondering why is a binary tree required when we have something such as a linked list understand that binary trees provide an ample number of advantages whenever you’re using the linked list and of course, it is considered as an extended linked list for the same reason as well.

Simple Example link:- Introduction to Binary Tree Data structure

**Ten** questions are what is the meaning of a **stack**.

The data type “Stack” is abstract, with a bounded(predefined) size. There is no difference between it and a list in that it allows adding and removing elements in a particular order. If you add a new element, it will go to the top of the stack, so the only element you can remove from the top of the stack is the element you’re currently looking at.

a stack is literally again as the name suggests like a stack of books or a stack of cards you know this is another data structure which has been implemented based on real life implementation so whenever you’re working with cues you have to understand the data can exit enter and do anything it wants in both of the directions left and right front and back when you’re working with a stack data can only be worked within one point of time for example if you are if you’re placing 10 books on your table right now you know to linearly and easily pick up a book it can only be done from the top element of course you can slide your hand under and pick up any number of books you want as well but that is not the concept here the concept here is that to easily able to lift without disturbing other books so in that particular case you can only lift the top one and once that’s gone you can pick up the next one right so if you think about putting the books in order if you put book number one it will be at the bottom most two is on it three is on two 4 is on 3 and so much more so if you have to delete an element you have to pick up the 10th book first the last book which went in is the first book that comes out again we’re gonna discuss this concept in the next couple of questions and with this pretty much stack implementation is again very easy and of course we’ll be looking at that in detail as well enough.

Simple Example link:- Stack Data Structure

**Eleven** questions are what is the working of **L.I.F.O.**

LIFO Stands for “Last In, First Out.” LIFO is a method of processing data in which the last items entered are the first to be removed. This is the opposite of LIFO is FIFO (First In, First Out), in which items are removed in the order they have been entered.

L.I.F.O as you can look at it is an acronym that stands for last in first out L.I.F.O is basically telling the user or giving guidelines on how data is accessed and worked on because whenever you think about L.I.F.O think about two data elements and compare it if you put in something it means that if it goes in at the last it’s the first one to come out well now this sounds very similar to question number 10 right because in a stack of books the last entity is the first one to come out of course L.I.F.O is the governing principle of how a stack works as well if an element is pushed in at the last it’s the first one to come out and of course if you have a requirement where you want to get the first element stored then you have to retrieve all of the above once and then only the last one which is literally the first one will come out.

Simple Example link:- LIFO data structure

**Twelve **questions are what are **multi-dimensional arrays**.

A multi-dimensional array is an array of arrays. 2-dimensional arrays are the most commonly used. They are used to store data in a tabular manner.

as we discussed multi-dimensional arrays are these arrays which span in more than one single direction more than one single dimension it will have a specific set of rows and specific set of columns just like a matrix in real-world and with this comes more than one index variable because one index variable is for the rows one index variable is for the columns and more so the interviewer might ask saying why do we require multi-dimensional arrays well one good reason of why you would require multi-dimensional arrays is when you cannot represent an entity in just one dimension see whenever you talk about let’s say GPS coordinates latitudes and longitudes as you might have studied in your college or school days you know whenever someone asks for a location an exact numerical location you cannot just provide the longitude and say i am here you’ll have to provide a latitude and longitude to represent your position as well so these two entities cannot be split into two data steps right to bring it together into one is again a very very good example of a two dimensional array enough.

Simple Example link:- Introduction to Multidimensional Arrays

**Thirteen** question number is Which is best in linked lists **linear** or **non-linear data structures**.

A linked list is non-linear in terms of storage. Based on how they are accessed, linked lists are linear.

I would say that it’s the best of both worlds here since it can be used in both a linear as well as a non-linear fashion where you know position data can be collected and randomly retrieved based on the storage policies as well as the retrieval procedures used, and this is fundamentally what gives it a linear behavior as the name implies. So whenever people ask me that question make sure to tell them that it can be used both linearly as well as non-linearly.

**Fourteen** question number is asked about what is a **binary search tree**.

Binary trees allow each node to have a maximum of two children, but there is no need to maintain the order of the nodes according to their values. During the construction of a binary tree, elements are ordered from top to bottom and right to left.

We saw what a binary tree was well what is binary search tree well again just like a binary tree it consists of two primary nodes from the root node so one root node where one of the child nodes is on the left and one of the child nodes is on the right but if you’re talking about binary search tree and since the data is always numerical whenever we work with trees the value in the left structure of the tree will always have to be less than the root node and values on the right side of the root node will always have to be much higher than the root node itself this is one key thing that makes a binary search tree what it is let’s say if you have 5 as the root node elements between 1 and 4 will compulsorily be on the left subtree and elements 5 and above will be compulsorily on the right subtree and then again in another step where you’re breaking the tree into further sub trees the same rule applies the primary root node of that little subtree that you that we’re talking about should have values which are you know greater than the left sub tree and less than the right subtree as well and this is how the entire tree can be generated.

Simple Example link:- Binary Search Tree

**Fifteen** question number is what is the meaning of **F.I.F.O.**

**FIFO** is an abbreviation for **first in, first out**. It is a method for handling data structures where the **first element** is processed first and the **newest element** is processed last.

FIFO FIFA is basically again just as I mentioned you’ll be served first based on the order and it’s pretty much as simple and as straightforward as that right and you can, of course, take a real-life example to show the interviewer that your understanding on the concept of e4 is concrete.

Void is used to indicate that a function/method does not return any data type. Null indicates that a pointer variable is not pointing to any address.

both of these sound very similar right void is like you know deficient of something and null says nothing so basically there is a small difference whenever you talk about void which basically says that whenever you know there is a certain data that has been declared let’s say a variable has been declared but then if it does not have any value associated with that while initializing it means that you know there is no size it means void in computer science terms now when we talk about null is actually a value it is a value which has no physical presence on the memory but then it’s seeing a particular state of that element or of that data structure at that given point of time why it says that there is no size involved but then null says that hey I am a value I have no physical presence but I denote something this is the very simple difference between void and null.

Simple Example link:- Void & Null Pointers Explained

**Seventeen** question number is stated what is **dynamic memory management**.

Dynamic memory allocation is a process that allows us to do exactly what we’re looking to do above, to **allocate memory while** our program is running, as opposed to telling the computer exactly how much we’ll need (and for what) ahead of time.

dynamic memory management is a way it’s a methodology where you know all of the storage units which are basically used to store your data changes what I mean by this is that as the name suggests since its dynamic memory is basically managed as in when the data occurs see if you buy a hard drive worth one terabyte but then if you only store data worth one gigabyte it won’t make sense now if you have to store one terabyte but then you have a hard drive worth one gigabyte it’s impossible right but then if you could find out what your data size is and if you could go on to match and store the data into the hard drive one element after another where let’s say size is unlimited in both of the cases this is exactly how dynamic memory management will work each of these entities gets separated or combined to eventually be stored and whenever you separate it or combine it they form entities called as composites and these composites are used as entities to be stored on the final data drive as well and these composites basically change in case if your data is changing so make sure you mention that.

Simple Example link:- Basics of Dynamic Memory Allocation

**Eighteen** question number is what are **push** and **pop** operations in data structures.

Push operation refers to inserting an element into the stack. Since there’s only one position at which the new element can be inserted — The top of the stack, the new element is inserted at the top of the stack. **Pop operation refers to the removal of an element**.

a push operation is when you add an element into the data structure a pop element is when you remove the element from the data structure so whenever you think of push think of it that you’re pushing an object into something else and whenever you’re pulling you’re removing one from the other this is the simplest way that you know used to remember push and pop operations way back in the day so whenever you think to push think of adding elements whenever you think of pop operation make sure that you’re thinking about the removal of data from the data structure it is as simple and straightforward.

Simple Example link:- Push and Pop Algorithm

**Nineteen** question number how is a variable stored in memory when one is using data structures.

whenever a variable has to be stored in memory you have to understand the value that is associated with the variable and how much of space is required to actually manage the memory for that particular entity so whenever your quantity of memory is assigned say you know what i need 10 bytes of data to store this value well firstly it depends on the data structure that’s being used and the second thing now whenever you’re using dynamic allocation what basically happens is that it understands and knows that 10 Giga 10 kilobytes might not be the only size that the variable will have it can have 10 it can have 100 it can become 1 gigabyte in 10 seconds and much more so a dynamic allocation will ensure that it will provide ample number of space and it will provide the extra legroom which is required to store this variable and in fact even if there are rapid changes in the size of the variables then it can be stored very well in an effective and an efficient way as we discussed in the previous couple of questions as well.

**Twenty** question number says what is **merge sort**.

Merge Sort is **useful for sorting linked lists**. Merge Sort is a stable sort which means that the same element in an array maintain their original positions with respect to each other. The overall time complexity of the Merge sort is O(nLogn). It is more efficient than it is in the worst case also the runtime is O(nlogn)

as the name suggests you might guess that since it has the word salt it has to do something with sorting elements in a certain order merge sort is one of the well merge sort is one of the popular methods used to sort elements whenever you have a linear data structure now let’s say you have an array where you want to perform merge sort on it the main working of merge sort is this divide and conquer approach this this is one very important thing that it’ll add a lot to your candidacy if you mention it’s the divide and conquer approach so basically each of the entities in a merge sort will be adjacent to each other right so the elements will be next to each other and these entities will get merged together and these tiny mergings or these tiny entities which are formed after merging will be sorted and then these sorted lists are kept aside another couple of elements are taken these elements are again sorted and then put to the left think of it like taking a chunk of you know colored rubber bands and then sorting each of these color rubber bands into their final color and then putting it aside into separate units well that is how merge sort works as well you merge a certain set of elements you sort it out you put it to the left you take in more merge it out sort it and put it to the left and now when you combine the entire thing everything from the left to the right will have been sorted because you you took individual elements and sorted it right that is how much sort works.

Simple Example link:- Merge sort algorithm

**Twenty-one** question number why should **heap** be used over a stack.

heap is another wonderful data structure which is very helpful whenever you are working with data structures because it is it provides the user with an efficient way to work with again when we talk about data structures when we talk about efficiency you might be wondering is this have to do something with dynamic memory allocation yes heaps are data structures which are basically used whenever you have the data which is which which requires a dynamic memory allocation requirement and you know where the data is removed and added as per the requirement as well so the access times and the storage methodologies the policies and whatever govern heaps in the back end of it which of course you should not be concerned with at this point of time makes it very effective it makes it very efficient to use you know whenever you compare it to a stack but then with the extra added dynamism with the extra efficiency in read write speeds if you’re directly compared to a simple stack the memory time is actually a bit slow why is this a bit slow because you have to have all of these things working in the back end to make sure that dynamism is maintained it is efficient no wonder a complex heap will be efficient when you compare it to a simple stack but then if you’re very hardcore about time complexity then of course a simple stack.

Simple Example link:- Why is the heap faster than the stack

**Twenty-two** question number what is the meaning of **data abstraction**.

Data abstraction is **the reduction of a particular body of data to a simplified representation of the whole**. Abstraction, in general, is the process of taking away or removing characteristics from something in order to reduce them to a set of essential characteristics.

data abstraction is again it’s a tool it’s a concept that is widely used in data structures data abstraction is a concept of simply breaking down complex entities into smaller problems than solving each of these smaller problems and then eventually you will have the solution to the entire problem so this approach this tool this technique is called as data after abstraction where you take a complex entity you break it down into simpler steps and further you can break down those simpler steps into even simpler steps to work with so so it isn’t the similar working analogy as a tree data structure you have one entity you break it down into two you break the two into four and much more right to think of it like that.

Simple Example link:- data abstraction

**Twenty-three** question number what is the meaning of a **post-fix expression** in data structures.

postfix prefix and in order traversals are very important questions that are asked in data structures interviews so make sure you understand each of these coming to postfix expressions first where postfix expression is a situation where you know each of the operators that you have whatever operation you’re performing on operator plus-minus multiplication division whatever it is this is basically preceded by its operands are the numbers which you’re working with so you know why we use prefix postfixes to basically ensure you know we have to remove all of the parentheses or to remove any sub-expressions wherever you’re working with operators because this forms a very vital thing number one with operator precedence number two if you’re performing any sort of lexical analysis, you know you’re working with postfix and prefix expressions will add a lot of value there as well.

**Twenty Four** question number is what is the working of **selection sort**.

Selection sort as the name suggests is another way of how you can assort the data selection sort is very simple let’s begin directly by understanding how it works so whenever you’re working with selection sort let’s say if you’re working in the ascending order it’ll directly select the smallest entity first inside the entire array after traversing it once and it’ll make sure to set the index of that to zero so what happens is as soon as that is done this element will be compulsory the first element next it finds the next smallest element puts it on the right of the first one next again look at the entire thing find the smallest one put it in its final place iterate through the entire thing find find the next smaller one put it at its final place so basically by iterating through the entire array you will be putting the element directly into its final position by putting an index value to that this is what is the simple working of a selection sort and of course that is done until all of the elements are sorted.

**Twenty Five** question numbers are what are **signed numbers** in **data structures**.

Signed numbers again pretty simple as the name suggests are it is numbers that will have a bit in the beginning which will basically denote if a number is a positive number or a negative number this bit that we’re talking about it is called a sign bit and since we’re talking about the binary if the signed bit is 1 it means that the number is negative in terms of binary and if we talk about the signed bit is 0 it means that whatever number you’re working with is a positive number so if the signed bit is 1 it indicates a number is a negative number and the presence of a negative sign if the signed bit is zero it indicates the presence of a positive sign and with this.

**Twenty-six** question number is stated what is the minimum number of **nodes** that a** binary tree** can have.

before we move on to the question can you take a quick guess about the minimum number of nodes a binary tree can have well you might have guessed right a binary tree can have 0 nodes, yes 0 nodes, or of course, a minimum of one and two as well so a binary tree is not a compulsion of having two compulsory nodes a binary tree with just a root node is also a binary tree with one node is a binary tree with two nodes is another binary tree as well and if you’re thinking about how it can be zero in another perspective if there is a null value in your tree as the root node again your binary tree is considered to be having zero nodes as well.

**The twenty-seven** question number is what are the data structures that make use of pointers.

pointers these these form to be very very important foundational concepts you have to know whenever you talk about data structures so pointers are basically entities that are used to point to an address of an element well why do you want to point to an address of an element to make sure that you can access it from one part of the memory right so let’s say if you want to visit your friend who’s staying probably one or two roads away and if you know just the person but you’ve never gone home you will need an address to go to meet this person right this is exactly how data works as well you need the address of where the data is located to work with it to pick it up deleted add to it remove it so pointers are used for that and then when we talk about the use of pointers in data structures it is used everywhere binary trees linked list stacks and queues to denote where each of the elements are and what are the addresses of each of those elements present in these concepts so make sure you mention at least three or four data structures where pointers are used

**The twenty-eight** question number is what is the use of **dynamic data structures**.

The concept of dynamic data structures is similar to that of dynamic memory allocation. Throughout this process, the entire structure provides the user an easy way to store and manipulate data, based on the amount of data that can be accessed, altered, or even manipulated, so it’s very flexible in that you can change it based on the amount of data.

A dynamic data structure is very similar to dynamic memory allocation in its working, as well as before it enters into an algorithm for processing or before a program is executed, so I’m pretty sure you can elaborate on this as well.

**The twenty-nine** question number is what is a **priority queue**.

we know what is the queue but what is a priority queue well a priority queue as the name suggests is a queue where each of the elements have a priority associated with it now let’s say you’re stuck in the traffic each of the cars that will move is one after the other right now if there is an ambulance or an emergency vehicle a firefighter vehicle in the back we’re pretty sure that all of us will move to the side and give the highest priority to these firefighters or ambulance vehicles right this is exactly how it works here in data structures as well so a priority queue is basically a regular queue but then each of these elements have a priority if an element has a higher priority it means that it will get processed quicker than the enabled with lower priority so when you’re working with priority queues well it can be a question on its own what is the minimum number of queues that are required to work with a priority queue well you will have a queue for the data and you will have another queue where you’re storing the priority of the associated data you get it right you have 10 elements on the left side which is the actual data but then you have another queue on the right side which is basically another 10 entities but then this is not the data but the priority to the data so if you ask the question saying what is the minimum number of queues required to implement a priority queue make sure to answer it as two it is not just two it’s a minimum of two you can of course have more but then these get into the complete range of complex data types but at this point of time make sure you answer it as a minimum of two.

**Thirty** question number is that **pointers** allocate memory for data storage **true or false**.

we discussed pointers to be addressing elements right of course the answer is false good pointers basically work as we discussed by pointing the governing data handler to a basic respective location it does not mean it can allocate memory on its own well memory is allocated entirely in a different procedure but then the memory is later accessed using pointers we hope that’s clear to you and of course whenever you talk about memory processing will mostly work in a dynamic environment only when the program begins its execution because only when you executing it you will understand what the data is required how much data is required and if it changes how you can handle that as well so the answer to if pointers can allocate memory for data storage is true or false is false

**Thirty-one** question number states what is the meaning of **DQ**.

we saw a regular q but then what is a DQ is very simple it’s a double-ended queue which means that you know very simply you can add or remove data from any of the two sides of a queue of course if you’re wondering if we can implement this on a regular queue yes you can implement this on a regular queue and basically use it as well so what this does is when you’re talking about the linked list as a concept of a doubly-linked list or circular linked list here you will have the data element and a left pointer and a write pointer and much more so whenever stuff is double-ended where data has to be accessed from both the ends or removed from both ends you will be implementing a DQ or in short for double-ended queues.

**Thirty-two** question numbers differentiate between **linear** and** non-linear** data structures.

we discussed this in the very first couple of questions right now let’s get back to that in a little more detail as i have explained before a linear data structure is when data is stored next to each other in a linear fashion in an ordered fashion a non-linear data structure is basically an entity which is stored in memory where it’s not next to each other and it’s stored in a haphazard way when you look at it literally see for example each of the elements in arrays linked list stacks and queues are stored right next to each other of course they can be stored anywhere in the memory we’re not talking about the addressing of the memory we’re talking about them literally being there so in linear the data is stored next to each other but then we’re talking about graph data structures when you’re talking about tree data structures it is non-linear and of course when you go on to visualize both of this you will get a clear cut picture of what the difference is between a linear data structure and a non-linear data structure but then one important thing make sure you give examples to support your claim and your understanding of this as well with this

**Thirty-three** question number is stated what is the meaning of an **AVL** tree.

we saw regulatories we saw binary trees and we saw binary search trees now what an AVL tree is basically a simple type of a binary search tree itself well an AVL tree is different from a regular binary search tree in a way where it’s only one part of it is being balanced not the entire tree it has a compulsion to be balanced like a binary search tree now then what is balance is basically when you’re directly comparing the height of the subtree from its main root node right so if the balance is off in a binary search tree it can be considered as an AVL tree but then if the entire tree is perfectly balanced and if it’s non-ambiguous it can be called as a binary search tree that is a simple AVL tree.

**The thirty-four** question number is how **Huffman's algorithm** work.

this is a very widely asked question the Huffman's algorithm basically whenever you’re working with Huffman’s you have to understand that you will require one table which is which stores the frequency of occurrence of elements if you have like 10 elements occurring 100 times each you have one table in Huffman's algorithm which will basically state that hey this element is occurring these many times so why is this required well whenever you’re constructing extended binary trees which will have a lot of subtrees or which will have only minimum weights you will require a method to keep a track of this easily Huffman's algorithm is the governing entity which helps you keep a track of this by giving you the frequency of occurrence where each data entity or each element presence is denoted in a structured format and of course you can explain this with an example using a sheet of paper as well that’ll add a lot to your candidacy.

**Thirty-five **question number is what are **recursive algorithms**.

A recursive as the name suggests is a methodology where you’re doing something in an iteration you’re doing something again and again in a loop so a recursive algorithm is an algorithm that will work by taking a complex problem and solving and simply breaking it down into simple problems and these simpler problems are worked on in a loop format in an order one after the other to solve the eventual big problem so when you talk about recursion the output of stage one of recursion will be the input of a stage two of the next loop of the same thing now the output of the second one is the input of the third one so that is how recursion operations work whenever you talk about recursion you’re talking about looping and you’re talking about how data moves in such a way that the complex problem can be broken down into simple entities and handled at the easiest way possible.

**Thirty-six** question number states how **bubble salt** work.

bubble sort is basically another sorting methodology which is applied to arrays where elements are next to each other and values are compared the working of bubble sort comes when the adjacent elements two elements right next to each other’s are compared and based on where they’re supposed to go they’re directly changed this is a very simple understanding of let’s say if you have an array which says one three two four now you compare one and three three is greater than one so the three stays right there now you compare three and two you realize that three is greater than two but three is on the left side but two is on the right side so you just swap three and two and that’s how it works in an iteration that is how a bubble sort works simply and so if you’re wondering why a bubble sort is called as a bubble sort it’s only because of the direct comparison between a bubble floating to the top of the water but then anything heavier than a bubble will of course be sinking to the bottom end right if you’re thinking of comparing a bubble to a rock and when you throw it inside the water the bubble will float above the water while the rock will sink this is exactly the working of how you know you can understand the working of bubble salt as well.

**Thirty-seven** question number is which is the fastest** sorting algorithm**.

this question is very subjective because there is no fastest you know sorting algorithm that’s available which is one algorithm that does everything so when you’re working with data structures you’re trying to solve a very specific niche problem this particular problem that you have can be sorted very quickly using bubble sort but then you will have another program or another problem you have to solve which can be solved the fastest using merge sort so the answer here is that it directly depends on the data and how the algorithm process processes the data as well so there is no single faster sorting algorithm it is based on your data it is based on how the algorithm works as well and the concept of time complexity is used here to track what is efficient and what is not with this.

**The thirty-eight** question number is what is the **postfix form** of the expression.

*An expression is called the postfix expression if the operator appears in the expression after the operands. Simply of the form (operand1 operand2 operator). Example : (X+Y * (Z-C) Given a Prefix expression, convert it into a Postfix expression.*

you see above that x plus y is multiplied by z minus c now again if you remember we’ll be doing the prefix and the postfix expressions to basically keep the data ready for lexical analysis and to remove the presence of our brackets as well so here the operators are preceded by their operand as you can see x y then we have the plus and then we have z c where we have the minus after the operands and then, of course, there is a simple formula there is a simple structure that will be used to work with postfix expression as well so my advice here is to make sure you take up certain examples on your own see what the formula is and then solve it because if you are given a random prefix or if you are given a random expression by the interviewer to solve you will, of course, have the ability to do so as well but then to keep it simple in this question we have given you the output as is but then make sure to practice on how you can get here as well with this.

**Thirty-nine** question number is, **where are tree data structures** used.

Again, as with all of the other data structures, even tree data structures are used in a lot of places and but then these are some of the most significant places where tree data structures are sort of championed you know whenever you’re working with lexical analysis whenever you’re working with hierarchical data modeling step by step data handling whenever you’re working with something called as a symbol table where when you’re creating a symbol table you will be using a tree and then whenever you’re handling basic arithmetic expressions as well three data structures are used if you want to perform traversals in order pre-order post order you can do so in a tree data structure as well so whenever you ask this question make sure to give two or three examples of where it’s used of course more than three will again be an added advantage but make sure that they are accurate and not incorrect.

**Forty** question number is what are the data structures that are used to implement **graphs**.

whenever we have to implement a graph we are literally not drawing a graph using programs here right a graph is a data structure now when you have to implement something called a graph you need two important data structures here to implement one data structure such as a graph basically we’re talking about adjacency matrix and adjacency list so adjacency list is used to tell you and it’s used to represent the data which will go in and the adjacency matrix is to basically tell you if your data is sequential and how you can go on to work with and store the sequential data as well so whenever you’re asked saying how are graphs implemented to make sure you mention two things adjacency matrix which will show you the sequential data representation adjacency list which is used to represent linear linked data well this is pretty much the most straightforward answer.

**Forty-one** question number is what are the data structures that are used in **DFS and BFS **algorithms.

DFS and BFS are again acronyms for depth-first searching and breadth-first searching these are again concepts that are used to perform search operations when you’re working with data structures but then know this when you’re working with depth-first searching you will be basically using the stack data structure and whenever you’re using breadth-first searching or the BFS technique you’ll be using a queue so if it’s depth-first it’s stack if it’s breadth-first or BFS it’s going to be qs and of course, if you think this might get a little confusing just try to see if you can find a way to remember it in a simpler way but then if you have actually worked with data structures or if you’ve had experience using these then it becomes quite literal of why a stack is used in the in DFS and why a queue is used in terms of BFS.

**Forty-two** question number is what are the **time complexities** of linear search and **bind research**.

as i mentioned linear search and binary search are basically two forms of searching and each of these are different and it’s working and of course binary search is more efficient than a linear search and how do you track this part where you say it’s more efficient right so basically with time complexity we can understand that with each iteration of how the working is so we have something called as the big o notation where we consider how fast an entity can execute for that particular values as well so at this point of time to answer the question and to keep it concise basically you need to understand that the time complexity for a linear search is of n but the time complexity for a binary search o of log n and then o of login you don’t have to explain the working of it it’s basically the working on the logarithmic scale but then in quite literal terms it means that the binary search is of course more effective it takes lesser comparisons to search and it takes eventually lesser time to find an element in an array.

**Forty-three** question number is, where are **multi-linked** data structures used.

multi-linked data structures are very very important in two concepts when we talk about multi-linked data structures make sure you understand that it is used wherever you require a sparse matrix and wherever you have to create an index you know wherever you have to perform any operations with respect to data handling so when we’re talking about sparse matrices and if you answer this question with saying that you know it’s used to generate a sparse matrix the interviewer might ask you what a sparse matrix means so basically a sparse matrix is a matrix which will contain minute number of non-zero elements so it is a matrix which will consist mostly of zeros and less number of actual elements so it’s basically used as a way in numerical analysis and scientific computing where it denotes only the functional components which are required in the matrix while omitting the ones which are not so in case if you are asked the follow-up question saying what sparse mattresses are you can use that answer as well so coming back to the original answer a multi-linked data structure is used in the generation of these sparse matrices and it is used whenever you have to create indices for data entities as well.

**Forty-four **question number is what is the methodology that is used when you have to perform in** order traversal**.

in order, traversal is when you have an operand on the left the operator on the middle side and another operand on the right side in case you’re working with binary operations but then if you’re working with trees the question is asking you about trees right of course when you talk about traversals mostly it’s trees so the tree is basically sought out and traversed through from the leftmost subtree after the left subtree is processed the root node is processed after the root notice process the right subtree is processed this is how it will work in the individual subtrees as well as the entire tree on the hole as well first the left is processed the middle element of the root element is processed next to the right element is processed now if you break the tree down into another subtree again there left center and right is the rule which will go on for the working of inorder traversal as well.

**forty-five** question is what is **post-order traversal**.

post-order traversal is basically when you traverse the left subtree first and then instead of going to the root node you will traverse the right subtree after the right subtree is visited you will come back to the root node so instead of left root and right as in order traversal with respect to post or traversal you’ll do the left first the right subtree the second and then come to the root node so make sure you understand these subtle differences because understanding them and remembering them is simple but once you start implementing them it can slightly get confusing.

**Forty-six** question number states are there any disadvantages to using **cues** or disadvantages when you’re implementing cues when you’re using an array.

whenever using an array you need to understand that with cues you might have any number of elements which can be added or removed right so if you if you’re standing in a queue at your favorite restaurant to get your table or something it’s not like five people stand there at a time right so you can have five people standing ten people standing hundred people waiting for their tables so with array whenever arrays are created their sizes are not dynamic by default of course you can have dynamic allocation but then so it provides a situation where there is a discrepancy when you’re creating the correct array size because you do not know you know how long your queue is going to be and in this case how many elements you will have when you’re working with queue that’s one disadvantage the second disadvantage is that whenever you store queue elements right it cannot be reused so it’s basically stored once and if you have to reuse it you cannot why is this because whenever you think about working with cues literally as the name suggests insertion will happen only at one point of time when you insert data at that point of time and keep pushing your data one on to the other you can use it only once you can use you cannot use it more than once and this will cause memory dumps where you’re just making it a little less efficient because you are adding a bit of discrepancy here too so whenever you asked about the disadvantages of implementing cues using arrays make sure you talk about array sizing and memory dumps.

**The forty-seven** question number is how can elements be inserted into a **circular queue**.

a circular queue is where one entity of the queue is connected on to the other and the last one eventually is connected to the first one as well so it’s placed in a circle now if you’re asked about how elements can be inserted in a circular queue make sure that you talk about two conditions two cases you know so basically the first case is when the front value is not zero but then the rear value is is max minus one when you talk about this particular condition it means that your q is not full that is what rear is equal to max minus 1 states if your q is not full then of course you can start implementing and inserting elements at that point of time the second case is where your rear is not equal to max minus 1 if rear is not equal to max minus 1 it means that the q is not full and this indicates that you know you can add on the values wherever you require but then since we’re talking about cues you will only have to add these values at the rear end of it that’s why we’re checking if the rear end is either less than max minus one or in fact if it is not equal to max minus one now these concepts are if you’re wondering about why it’s been named like this this is exactly a standard structure that is used to go on to write code with respect to handling queues or handling stacks or any other data structures as well of course it can be anything else but then to keep a readability at its high the namings have been given as such so make sure you mention these two cases.

**Forty-eight** question number is stated what is the use of **void pointers**.

we know what regular pointers are they’re used to you know basically address and index and element in memory now what is a void pointer a wire pointer is basically an entity that is used you know whenever you have to store another pointer well a regular pointer points to data a void pointer will point to another pointer it has the ability to store up data of a pointer right so it’s pretty simple it’s so it’s as simple as that a void pointer is an entity which is used whenever you have to store another pointer inside of it and this pointer which gets stored inside of course it can be a regular pointer where it’s pointing to the variety of data that it is usually used for so where is void point used well it’s basically used whenever you have to implement multiple linked lists which are not similar to each other as well so now if you have a case of implementing these linked lists in multiple programming languages and if you have to bring it together at once you will require one pointer which is pointing to another one where you’re storing the data of that pointer as well in that particular case you will be using a void pointer.

**The forty-nine** question number is what is the meaning of the **stack overflow** condition.

stack overflow as the name suggests that your stack is literally overflowing it means that your stack is completely full and whenever you insert any more elements it is not possible to store them into that stack so an easy way to remember this is to remember a stack which is actually overflowing so let’s say you have 10 books in your locker or you have 10 books on your bookshelf but then you don’t have space for the 11th one so this means that you have hit a stack overflow condition, of course, this is a direct analogy of how it works here as well but then if you’re asked to state the condition where stack overflow happens well when the top element is equal to the max size minus 1 it means that the stack is full and the overflow condition has been hit so make sure to mention that condition.

**Fifty** questions are about what is **Divide and conquer algorithmically**.

Divide and Conquer is an algorithmic pattern. It is common for algorithmic methods to focus on a huge input issue, divide the input into small pieces, solve the problem on each of the pieces, and then merge the pieces into a global solution. This mechanism of solving the problem is called the Divide & Conquer Strategy.

So that’s all the questions or topics that I think it's important for any interview and also for us. those details collect from many websites like geeks for geeks, tutorials point, etc so thanks all for those. if you found any other point that is needed for us also if somewhere is miscorrected information, please describe it in a comment.