Data Structures interview questions are crucial for any programming interview, particularly for Java and Data Science-based job roles. Many times, computer science professionals undervalue the significance of studying data structures and algorithms, considering it as complex or a waste. But, they soon understand the reality when they start job haunting. Leading tech giants like Google, Amazon, and Microsoft frequently ask data structures questions to evaluate the problem-solving skills of the candidate. So, prepare yourself for the tech interviews by getting through the below data structures interview questions. Our industry professionals curated the questions on data structures concepts like stack, queue, binary tree, linked list, sorting, array, etc.
Data Structures are the base of software engineering. Almost all coding interviews you face today are mainly comprised of Data structure and Algorithm based questions. And big organizations like Google, Amazon, Facebook, etc., are also hiring programmers who are exceptionally good at optimizing data structures.
If you're aspiring to take your career to the next level in Data Structures but are confused about what kind of programming questions you'll face in your interview? Well, you've reached the right place!
This article shares the latest 2023 Data Structure Interview questions and answers collected from different interviews for programmers at varying levels of experience. We hope the topic areas covered in this blog help both beginners and experienced crack their next interview!
According to research Data structures has a market share of about 9.4%. So, You still have the opportunity to move ahead in your career in Data structures. Mindmajix offers Advanced Data structures Interview Questions 2021 that help you in cracking your interview & acquire a dream career as a Data structures Developer.
|Types of Data Structure Interview Questions|
Below mentioned are the Top Frequently asked Data structures Interview Questions and Answers that will help you to prepare for the Data structures interview. Let's have a look into them.
The data structure is nothing but an entity where the data is perfectly aligned and can be manipulated as per the requirement. When we deal with data structure it is not just about one table of data but it is about different data sets and how well they are aligned with each other. Overall, it helps the data to be organized.
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The basic operations performed on data structures are as follows:
A linked list is nothing but a sequence of nodes. With this sequence, each node is connected to the following node. It forms a chain of data storage.
To reference all the elements in the one-dimension array, we have to use an indexed loop. With the help of this, it executes from “0” to array size minus one. By following this process the loop counter will be able to refer to all the elements.
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The data structure is a vital aspect while handling data. The following are specific areas where the data structure is applied:
The above are few areas where the data structure is applied and not limited to.
The way to write arithmetic expressions is known as notation. There are three types of notations used in an arithmetic expression, i.e., without changing the essence or output of expression. These notations are:
The following table briefly tries to show the difference in all three notations −
|Infix Notation||Prefix Notation||Postfix Notation|
|x + y||+ x y||x y +|
|(x + y) ∗ z||∗ + x y z||x y + z ∗|
|x ∗ (y + z)||∗ x + y z||x y z + ∗|
|x / y + z / w||+ / x y / z w||x y / z w / +|
|(x + y) ∗ (z + w)||∗ + x y + z w||x y + z w + ∗|
|((x + y) ∗ z) - w||- ∗ + x y z w||x y + z ∗ w -|
LIFO stands for Last In First Out.
This process describes how the data is accessed, stored, and then retrieved. So the latest data that is stored in the database can be extracted first.
It is one type of data structure that has two nodes, has left node and a right node. In a programming language, binary trees are considered to be an extension to the linked list.
The stack is considered as a data structure where the top layer element can be accessed. The data is stored in the stack and every time when data is stored, it pushes the data downwards which enables the users to access the latest data from the top layers.
Multidimensional arrays use multiple indexes in order to store data in the database. In a few scenarios, data cannot be stored using a single dimension index, in these scenarios multidimensional arrays are useful.
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This is purely determined on the requirement basis, a linked list can be considered as a linear data structure or a non-linear data structure. For example: If the linked list is used on storage, then the linked list is considered as a nonlinear data structure.
If linked lists are used against access strategies then they are considered as a linear data structure.
A dynamic memory allocation will help you effectively manage your data by allocating structured blocks to have composite structures that can be flexible, i.e. it can expand and can contract based on the need.
Also, they are capable of storing simple structured data types.
FIFO in data terminology stands as “First in, First Out”.
This process defines or depicts how the data is stored inserted and accessed in a queue. Within this process, the data that is inserted at the beginning of the queue will only be extracted or accessed first.
A merge sort is nothing but a process where the data is divided and sorted to reach the end goal. Within this process, the adjacent elements are merged and sorted to create bigger elements. These sorted elements are gathered again and made the even bigger list. This process is continuous and repetitive until and unless they have nailed it down to a single sorted list.
The important aspect or advantage of a linked list is that it is the perfect data structure where the data can be modified very easily. Also, it doesn’t matter how many elements are available on the linked list.
The two main activities, i.e. Pushing and Popping applies the way how data is stored and retrieved in an entity. So if you check in detail, a Push is nothing but a process where data is added to the stack. On the contrary, a Pop is an activity where data is retrieved from the stack. When we discuss data retrieval it only considers the topmost available data.
The amount of space or memory is occupied or allocated depends upon the data type of the variables that are declared. So let’s explain the same by considering an example: Let’s say the variable is declared as an integer type then 32 bits of memory storage is allocated for that particular variable.
So based on the data type of the variable, the memory space will be allocated.
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The advantages of the heap compared to a stack are listed below:
On the contrary, the disadvantages of the heap compared to a stack is listed below:
The following are the steps that you need to follow to insert the data into the tree:
A binary tree is allowed or can have a minimum of zero nodes. Further, a binary tree can also have 1 or 2 nodes.
The nature of the dynamic data structure is different compared to the standard data structures, the word dynamic data structures means that the data structure is flexible in nature. As per the need, the data structure can be expanded and contracted. Thus it helps the users to manipulate the data without worrying too much about the data structure flexibility.
While referring to array the data is stored and utilized based on the index and this number actually co-relates to the element number in the data sequence. So thus making it flexible to access data in any order. Within programming language, an array is considered as a variable having a certain number of indexed elements.
The minimum number of queues that are needed is two. Out of which, one queue is intended for sorting priorities and the other queue is meant for the actual storage of data.
The list of all sorting algorithms are below:
Out of the above sorting options, none of the sorting algorithms can be tagged as the fastest algorithm, because each of these sorting algorithms is defined for a specific purpose. So based on the data structure and data sets available the sorting algorithms are used.
A de queue is nothing but a double-ended queue. Within this structure, the elements can be inserted or deleted from both sides.
A selection sort is a process where it picks up the smallest number from the entire data setlist and places it at the beginning. The same process is continued where the second position is already filled in. The same process is continued all the way until the list is completed. The selection sort is defined as a simple sort algorithm when compared to others.
A graph is nothing but a type of data structure that has a set of ordered pairs. In turn, these pairs are again acknowledged as edges or arcs. These are used to connect different nodes where the data can be accessed and stored based on the needs.
Yes, you can implement a stack using two queues. Any data structure to act like a stack should have a push() method to add data on top and a pop() method to remove the top data.
Using two stacks, you can implement a queue. The purpose is to complete the queue's en queue operation so that the initially entered element always ends up at the top of the stack.
In data structures, you use LRU (Least Recently Used) cache to organize items in order of use, enabling you to quickly find out which item hasn't been used for a long time.
To implement the LRU cache, you should use two data structures: a hashmap and a doubly linked list.
A hashmap helps with the lookup of cached keys, and a doubly-linked list helps maintain the eviction order.
Array and Linked list are two ways of organizing the data in memory. The below table lists the various differences between the array and linked lists:
|An array is a sequence of elements of a similar data type.||A Linked list is a set of objects known as a node, where it internally consists of two parts, i.e., data and address.|
|It can be accessed irregularly using the array index.||Linked lists support random access. Only supports sequential access.|
|Array elements store in contiguous locations in memory.||New elements can be stored anywhere, and a reference is created for the new element using pointers.|
|In arrays, memory allocation is done during compile time.||In linked lists, memory allocation is done during runtime.|
|Array size must be defined at the time of declaration/initialization.||Linked list size grows when new elements are inserted or deleted.|
The following are the advantages of Linked Lists over Arrays:
Following are some of the essential applications in a data structure:
The following are the different types of Linked Lists.
The main difference between storage structure and file structure depends on the memory area that is accessed.
Mainly the following operations are performed on a stack:
A linked list is a series of data structures connected via links. In simple words, it's a sequence of links that contain items. After the array, the linked list is the second most used data structure. The essential terms to understand the linked list are:
Link - In a linked list, each link stores data called an element.
Next - In a linked list, each link is connected to the following link called next.
LinkedList - It contains the connection link to the first link called first.
The below diagram depicts how nodes are connected in the Linked List:
Basic operations supported by a linked list:
It depends on where you plan to use Linked lists. You can consider a linked list for both non-linear and linear data structures. When used for data storage, it is regarded as a non-linear data structure. When used for access strategies, it is considered a linear data structure.
A doubly linked list is one of the complex types of the linked list, where a node contains a pointer to the previous and the next node in the sequence. It consists of three parts: node data, a pointer to the next node in sequence (next pointer), a pointer to the previous node (previous pointer)
The below diagram depicts the working of doubly linked list:
Some of the real-time applications where doubly-linked lists used are navigation systems and browsers (when both back and front navigation is required).
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A queue is a linear data structure that supports a specific order in which operations are performed. The order is FIFO (First in First Out) methodology, i.e., data items stored first will be accessed first. Unlike stack, the queue is open at both ends, and one end is always used to insert data and another one to remove data.
The basic operations associated with queues -
As the name suggests, the queue is used whenever you need to manage a group of objects in the order FIFO. A few of the queue data structure applications are listed below:
Both stack and heap are used for memory needs. The stack is primarily used to save the method execution order and local variables, and always follow the LIFO order.
Whereas heap is used for dynamic allocation and deallocation of memory blocks. It stores objects in Java. Memory allocated to the heap lives until one of the following events occurs:
The size of heap memory is more when using recursion when compared with the stack, as it quickly fill-ups stack memory.
Applications of graph data structures in real-time are:
An AVL (Adelson, Velskii, and Landi) is a self-balancing binary search tree where the variation of heights of the right and left subtrees of any node is not more than one.
An example of an AVL tree:
A tree is balanced if the balance factor of each node is between -1 to 1. Or else, the tree is unbalanced and needs to be balanced.
Jagged arrays are a particular type of arrays used for storing rows of data of varying lengths to improve efficiency when working with multidimensional arrays.
A max heap in a data structure is a complete binary tree where each internal node's value is greater than or equal to that node's children's values.
You can find the height of a binary tree using a recursive Depth-First Search (DFS) algorithm, as shown below:
By now, you must have got enough idea about what kind of Data Structure Interview Questions you can expect in your interview. Although you can expect many of these questions, you need to spend some more time on your learning curve. There is no point in attempting these questions if you don't have enough knowledge of essential Data Structures and Algorithms.
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Ravindra Savaram is a Technical Lead at Mindmajix.com. His passion lies in writing articles on the most popular IT platforms including Machine learning, DevOps, Data Science, Artificial Intelligence, RPA, Deep Learning, and so on. You can stay up to date on all these technologies by following him on LinkedIn and Twitter.
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