Data Structures and Algorithms are among the most important subjects in the world of Computer Science and Engineering. If you appear for a software engineering interview, you can be sure to face a round of questions specially dedicated to Data Structures and Algorithms – that is how crucial they are!

Algorithms lie at the core of everything that happens in computer science and data science. From Machine Learning to AI to Blockchain – all technologies run on algorithms. And algorithms need Data Structures to function. Thus, the combined knowledge of Data Structures and Algorithms can help you stand out from the crowd during your interview.

However, the challenge is that DSA is an extensive domain. Here, learning never stops, and there is always some new development that you need to understand. While continual upskilling is mandatory when dealing with Data Structures and Algorithms, today, we’ll look at some DSA fundamentals that will help you ace technical interviews.

Table of Contents

**Top Data Structures and Algorithms Interview Q&As**

**What do you understand about ‘Data Structures’?**

Data Structures can be defined as techniques used to define, store, and access data systematically. They form the most important component of any algorithm. Depending on the type of Data Structures, they store different kinds of data and are accessible in different ways. For an algorithm to return a result, it needs to operate on and manipulate a set of data structures in an organised and efficient manner to come to the final result.

**How can you differentiate between a File Structure and a Data Structure?**

In File Structures, the data is stored on disks following standard file storage policies and is not compatible with external, third-party applications. In Data Structures, on the other hand, the data is stored both on the disk as well as RAM in customised storage policies, and these are highly compatible with external apps.

**What are the broad types of data structures?**

Data Structures can be broadly divided into two categories:

**Linear:**In this, all the elements are stored sequentially, and retrieval takes place linearly. The arrangement is non-hierarchical, and each element has one successor and one predecessor. Example – Arrays, Linked Lists, Stacks, Queues, etc.**Non-linear:**Here, the storage does not happen in a linear sequence – i.e., all elements don’t necessarily have just one successor and predecessor. Instead, elements in non-linear Data Structures are connected to two or more items in a non-linear manner. Example – Trees, Graphs, Heaps.

**4. What are some key usage areas of Data Structures? **

Data Structures are pretty much required in all the fields of computing that you can think of, especially Algorithms and Algorithm Optimization. Here are some other areas where Data Structures are extensively used:

- Operating system design
- Numerical analysis
- Machine Learning and AI
- Compiler design and development
- Database management
- Lexical analysis
- Graphical programming
- Searching and sorting algorithms, and more.

**Explain the Stack Data Structure and mention its usage areas.**

Stack is simply an ordered list that allows insertion and deletion only from one of the ends – which is known as the ‘top’. It is a recursive Data Structure that has a pointer to its ‘top’ elements which lets us know about the topmost element of the Stack. Based on the element retrieval strategy, Stack is also known as Last-In-First-Out, since the last element pushed into the stack will be at the top, and will be the first to be popped out. Here are some uses of the Stack Data Structure:

- Backtracking
- Memory Management
- Function return and calling
- Expression evaluation

**What are the operations that can be performed on a stack?**

The Stack Data Structure supports the following three operations:

- push() — to insert an element into the top position of the Stack.
- pop() — to bring one element out from the top of the Stack.
- peek() — to just check the element present on the top of the Stack without bringing it out of the Stack.

**What do you understand about Postfix Expressions?**

Postfix Expression is an expression where operators follow the operands. This is extremely beneficial in computing terms since it doesn’t require any grouping of sub-expressions into parenthesis or even consider operator precedence. The expression a+b is simply represented as ab+ in postfix.

**How are 2D array elements stored in the memory?**

** **The elements of a 2-D array can be stored in the memory in either of the two ways:

**Row-Major:**In this method, first all the rows of the array are contiguously stored in the memory. First, the 1st row is stored completely, then the 2nd row, and so on till the last one.**Column-Major:**In this, all the columns of the array are continuously stored in the memory. First, the 1st column is stored completely, then the 2nd column, and so on.

**Define Linked List Data Structure.**

Linked Lists are collections of nodes – which are randomly stored objects. Each node has two internal elements – a Data field and a Link field. The Data field holds the value that the particular node has, while the Link field has a pointer to the next node that this one is linked to. Depending on the situation, a Linked List can be considered both as a linear as well as a non-linear Data Structure.

**In what ways are Linked Lists better than arrays?**

Linked Lists are better than arrays in the following ways:

- Array sizes are fixed at run-time and can’t be modified later, but Linked Lists can be expanded in real-time, as per the requirements.
- Linked Lists are not stored contiguously in the memory, as a result, they are a lot more memory efficient than arrays that are statically stored.
- The number of elements that can be stored in any Linked List are limited to only the available memory space, while the number of elements is bound by the size of the array.

**In C programming language, which pointer would you use to implement a heterogeneous linked list?**

Heterogeneous linked lists, as the name suggests, hold different data types. As a result, it is not possible to use ordinary pointers here. So, Void pointers are normally used in such a scenario since they are capable of pointing to any type of value.

**What is a Doubly Linked List?**

As the name suggests, a Doubly Linked List is a Linked List which has nodes linked to both the succeeding and preceding nodes. As a result, the nodes of Doubly Linked List have three, not two, fields:

- The Data Field
- Next pointer (for pointing the next node)
- Previous pointer (for pointing the previous node)

**Explain the Queue Data Structure with some of its applications.**

A Queue is an ordered list that allows for insertion and deletion of elements from not one but two ends – called REAR and FRONT. The insertion takes place from the FRONT end while the deletion from the REAR end. As a result of this, Queue is often called First-In-First-Out (FIFO). Here are some widespread applications of Queues as a Data Structure:

- For waiting lists for singly shared resources like CPU, printer, disk, etc.
- For asynchronous transfer of data, example file IO, sockets, pipes.
- As buffers in most of the media player applications.
- In Operating Systems for handling interruptions.

**Can you list some drawbacks of implementing Queues using arrays?**

There are mainly two drawbacks that occur when implementing Queues with arrays:

- Mismanagement of memory, since arrays are static data structures so implementing Queues with arrays removes a lot of functionalities of Queues.
- Problem with size, since array sizes are defined during array definition. So, if we want to add more elements to our queue than the size of the array, it won’t be possible.

**What conditions should be fulfilled in order for an element to be inserted into a circular queue?**

Here are some relevant conditions regarding insertion into circular queues:

- If (rear + 1)%maxsize == front -> this means the queue is full -> no more insertion possible.
- If rear != max – 1, the value of rear gets incremented to maxsize and a new value will be inserted at the rear end.
- If front != 0 and rear == max -1 –> this means that the queue is not full. So, the value of rear gets set to 0, and a new element is inserted into the rear end of the circular queue.

**16. What is a dequeue?**

Double-Ended Queue or deque is an ordered set of elements that facilitates insertion and deletion from both the ends – rear and front. As a result of which, this is even more flexible than the queue data structure.

**Define the Tree Data Structure and list some types of trees.**

Tree is a non-linear, recursive data structure that contains various nodes. One particular node is designated as the root of the tree from where all other nodes emerge. Apart from root, all the other nodes are called child nodes. There are broadly the following types of Tree Data Structures:

- General Trees
- Binary Trees
- Binary Search Trees
- Forests
- Expression Tree
- Tournament Tree

**How does bubble sort work?**

Bubble Sort is one of the most used sorting algorithms, and is used with arrays by comparing adjacent elements and exchanging their positions based on their values. It’s called bubble sort because the visualization of how it works is like bubbles floating from top of the water and larger entities sinking down.

**Read: **Data Structures in C & How to use?

**Which is the fastest sorting algorithm available?**

There are many different sorting algorithms available, like merge sort, quick sort, bubble sort, and more. Out of these, we can’t pick one specific algorithm which is objectively the fastest since their performance varies greatly based on the input data, the reaction after the algorithm has processed the data, and how it’s stored.

**What are Binary Trees?**

Binary Trees are special types of trees in which each node can have AT MOST two children. To make things easier, Binary Trees are generally divided into three disjoint sets — root node, right subtree, and left subtree.

**How can AVL Trees be used in various operations as compared to BST?**

AVL trees are height-balanced trees, so they don’t allow for the tree to get skewed from any one side. The time taken for all the operations performed on BST of height h is O(h). However, this can go on to be O(n) in the worst case scenario – where BST becomes skewed. AVL helps in eliminating this limitation by restricting the height of the tree. In doing so, it imposes an upper bound on all the operations to be maximum of O(log n) where n = number of nodes.

**What are the properties of a B-Tree?**

A B-Tree of an order m contains the following properties:

- All the properties of an M-way tree.
- Every node of the B_tree will have maximum m children.
- Every node except root and leaf will have at least m / 2 children.
- The root node must have at least 2 child nodes.
- All leaf nodes must lie on the same horizontal level.

**What do you understand about the Graph Data Structure?**

The Graph (G) Data Structure can be defined as an ordered set G(V,E) where V represents the set of vertices and E is the edges that form the connections. Basically, a Graph can be thought of as a cyclic tree where nodes can maintain complex relationships between them and not just parent-child relationships.

**Differentiate between cycle, path, and circuit with reference to the Graph Data Structure.**

The differences between the cycle, path, and circuit are as follows:

- A patch is an order of neighbouring vertices connected by edges without any restrictions.
- A cycle is a closed path wherein the initial vertex is the same as the end vertex. In a cycle, no particular verte can be visited twice.
- A circuit, like a cycle, is a closed path with the initial vertex the same as the end vertex. However, any particular vertex in a circuit can be visited more than once.

**How does Kruskal’s algorithm work?**

Kruskal’s Algorithm considers the graph as a forest and each of its nodes as an individual tree. A tree is said to connect to another tree if and only if it has the least cost among all the options, and it violates no property of a Minimum Spanning Tree (MST).

**Related:** Binary Tree in Data Structure

**How does Prim’s algorithm find the spanning tree?**

Prim’s algorithm works by considering nodes as single trees. Then, it keeps on adding new nodes to the spanning tree from the given graph that has to be converted into the required spanning tree.

**What is a minimum spanning tree (MST)?**

MSTs, in weighted graphs, are trees that have minimum weight, but they span across all the vertices.

**What is a recursive function?**

By definition, a recursive function calls itself back or directly calls a function that calls it. Every recursive function has some base criteria, following which the function stops calling itself.

**What is the interpolation search technique?**

The interpolation search technique is a modification over the Binary Search method. The interpolation search algorithm works on the probing position of desired values.

**What is hashing?**

Hashing is a very useful technique used to convert a range of key-value pairs into indexes of an array. Hash tables come in handy while creating associative data storage in which data index can easily be found just by providing its key-value pair!

**In conclusion**

Data Structures are truly the foundation of everything else that happens in Computer Science. Even the more complex applications of Computer Science, i.e Data Science, Machine Learning, Artificial Intelligence, IoT are all built on top of Data Structures and Algorithms.

So, if you are a beginner looking to make a career in any of the new-age fields, you are recommended to begin by mastering Data Structures and Algorithms. Or else, you can also check out our course on Executive PG Programme in Data Science which is designed for beginners. Learn from scratch and become a Data Science expert. Get yourself enrolled today!

### Interviews for which job role generally ask Data Structure and Algorithm questions?

If you’re sitting for any software development or engineering role, you will definitely be checked on your DSA skills. Apart from that, if you’re applying for Data Science jobs or want to venture into Machine Learning, you will be expected to know DSA.

### Do I need to know programming in order to understand Data Structure and Algorithms?

No, not necessarily. DSA is mostly abstract, and it is all about the mathematics and representations and flow of what goes on behind the scenes of any application or program. While having experience with programming will come in handy when you implement Algorithms, it is by no means a prerequisite to studying DSA.

### Are Data Structures always static?

No, Data Structures can be both dynamic and static, depending on the way memory allocation works for them. For example, Arrays are static data structures since an entire lot of contiguous memory is allocated to them when they are defined. On the other hand, Linked Lists are dynamic Data Structures as they don’t have any fixed size and the number of nodes can increase depending on the programmer’s requirements.