Data Structure viva Question & Answers Semester 3

This set of Data Structure viva questions and answers provides a comprehensive overview of data structures and their applications. Starting with an introduction to data structures and abstract data types, the topics progress to cover stack and queues, linked lists, trees, graphs, and searching techniques. Each module includes several subtopics that are explained concisely, accompanied by relevant examples. The questions cover the fundamental concepts, operations, and implementations of different data structures

Module 1: Introduction to Data Structures

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Q1What is the concept of Abstract Data Types (ADT)?
A1ADT refers to a mathematical model that defines the set of operations that can be performed on a data structure, without specifying the implementation details. It provides a high-level description of a data structure’s behavior and properties. ADTs focus on what operations can be performed, not on how they are implemented.
Q2Differentiate between linear and nonlinear data structures.
A2Linear data structures store elements sequentially, while nonlinear data structures allow elements to be interconnected in various ways.
Q3What are the common operations performed on data structures?
A3The common operations on data structures include insertion, deletion, traversal, searching, and sorting.
Q4Give examples of linear data structures.
A4Examples of linear data structures include arrays, linked lists, stacks, and queues.
Q5How can you define a data structure?
A5A data structure can be defined as a way of organizing and storing data in a specific format to facilitate efficient operations.
Q6Explain the concept of well-formedness of parentheses.
A6Well-formedness of parentheses refers to the balanced arrangement of opening and closing parentheses in an expression.
Q7What is the postfix notation? How is it useful in evaluating expressions?
A7Postfix notation, also known as Reverse Polish Notation (RPN), is a way of representing mathematical expressions without the use of parentheses. It simplifies expression evaluation by eliminating the need for operator precedence rules.
Q8Define recursion and its significance in data structures.
A8Recursion is a programming technique where a function calls itself during its execution. It is useful in solving problems that can be divided into smaller subproblems. Recursion is commonly used in data structures for tasks such as tree traversal, graph traversal, and divide-and-conquer algorithms.
Q9What are the types of data structures based on memory allocation?
A9Data structures can be classified into static data structures (allocated at compile-time) and dynamic data structures (allocated at runtime).
Q10Explain the concept of an Abstract Data Type (ADT).
A10An Abstract Data Type (ADT) is a high-level description of a data structure that defines the operations that can be performed on it, without specifying the implementation details. ADTs provide a way to encapsulate data and operations, allowing the implementation details to be hidden and providing modularity and reusability in code.

Module 2: Stack and Queues

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Q1What is a stack? Explain its Last-In-First-Out (LIFO) property.
A1A stack is a linear data structure that follows the Last-In-First-Out (LIFO) principle, where the last element inserted is the first one to be removed.
Q2What are the common operations performed on a stack?
A2The common operations performed on a stack are push (inserting an element onto the stack) and pop (removing the top element from the stack).
Q3How can you implement a stack using an array?
A3A stack can be implemented using an array by maintaining a variable to keep track of the top element and using array operations to insert and remove elements.
Q4Explain the applications of a stack in real-life scenarios.
A4Some applications of a stack include function calls and recursion, expression evaluation and conversion, undo/redo functionality, and backtracking in algorithms.
Q5Describe the circular queue and its advantages.
A5A circular queue is a variation of a regular queue where the rear and front elements are connected. It allows efficient space utilization and avoids unnecessary shifting.
Q6What is a priority queue? How is it different from a regular queue?
A6A priority queue is a variation of a queue where each element has a priority assigned to it. Higher priority elements are dequeued first compared to lower priority elements.
Q7How can you implement a queue using an array?
A7A queue can be implemented using an array by maintaining two pointers, one for the front and one for the rear, to keep track of the elements.
Q8Describe the double-ended queue (deque) and its applications.
A8A double-ended queue, or deque, is a linear data structure that allows insertion and deletion of elements from both ends. It can be used in scenarios requiring both stack and queue operations.
Q9Explain the concept of linear search and binary search.
A9Linear search involves iterating through the elements of a collection until the target element is found. Binary search is a more efficient search algorithm that requires a sorted collection and repeatedly divides the search space in half.
Q10What is hashing and what are collision resolution techniques?
A10Hashing is a technique to map data to a fixed-size array index using a hash function. Collision resolution techniques are used to handle situations where two elements map to the same index, such as separate chaining and open addressing.

Module 3: Linked List

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Q1What is a linked list? How does it differ from an array?
A1A linked list is a linear data structure where elements are stored in separate nodes, each containing a reference to the next node. Unlike an array, linked lists can dynamically grow and shrink in size.
Q2Explain the concept of singly linked list and doubly linked list.
A2In a singly linked list, each node contains a reference to the next node. In a doubly linked list, each node contains references to both the next node and the previous node.
Q3What are the common operations performed on a singly linked list and a doubly linked list?
A3Common operations on a singly linked list include insertion, deletion, traversal, and searching. Common operations on a doubly linked list also include backward traversal and insertion/deletion at the tail.
Q4How can you implement a stack and a queue using a singly linked list?
A4A stack can be implemented using a singly linked list by performing push and pop operations at the head. A queue can be implemented by performing enqueue at the tail and dequeue at the head.
Q5What is the application of a singly linked list for polynomial representation and addition?
A5Singly linked lists can be used to represent polynomials, with each node containing a coefficient and an exponent. Addition of polynomials can be performed by adding corresponding terms of the linked lists.
Q6Explain the concept of a tree and its terminologies.
A6A tree is a nonlinear data structure composed of nodes connected by edges. Terminologies include root, parent, child, sibling, leaf, depth, height, and level of a tree.
Q7How can a binary tree be represented?
A7A binary tree can be represented using linked representations such as a structure with left and right pointers or an array-based representation using indices.
Q8What are the common tree traversal methods?
A8Common tree traversal methods include in-order, pre-order, and post-order traversals, which define the order in which the nodes are visited.
Q9What is a binary search tree (BST)? How does it differ from a regular binary tree?
A9A binary search tree is a binary tree where the value of each node in the left subtree is less than the value of the node, and the value of each node in the right subtree is greater than the value of the node. A regular binary tree has no specific ordering criteria for its nodes.
Q10What are the applications of binary trees, such as expression trees, Huffman encoding, and AVL trees?
A10Binary trees have applications in expression evaluation, Huffman encoding for data compression, and AVL trees for efficient searching and sorting.

Module 4: Trees

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Q1Explain the concept of a tree and its terminologies.
A1A tree is a nonlinear data structure composed of nodes connected by edges. Terminologies include root, parent, child, sibling, leaf, depth, height, and level of a tree.
Q2How can a binary tree be represented?
A2A binary tree can be represented using linked representations such as a structure with left and right pointers or an array-based representation using indices.
Q3What are the common tree traversal methods?
A3Common tree traversal methods include in-order, pre-order, and post-order traversals, which define the order in which the nodes are visited.
Q4What is a binary search tree (BST)? How does it differ from a regular binary tree?
A4A binary search tree is a binary tree where the value of each node in the left subtree is less than the value of the node, and the value of each node in the right subtree is greater than the value of the node. A regular binary tree has no specific ordering criteria for its nodes.
Q5What are the applications of binary trees, such as expression trees, Huffman encoding, and AVL trees?
A5Binary trees have applications in expression evaluation, Huffman encoding for data compression, and AVL trees for efficient searching and sorting.
Q6Explain the concept of an expression tree and its use in evaluating arithmetic expressions.
A6An expression tree is a binary tree where the operators are stored at internal nodes, and the operands are stored at the leaf nodes. Expression trees can be used to evaluate arithmetic expressions by traversing the tree and performing the corresponding operations.
Q7What is Huffman encoding? How does it work?
A7Huffman encoding is a data compression technique that assigns shorter codes to more frequently occurring characters and longer codes to less frequently occurring characters. It works by constructing a Huffman tree based on the frequency of characters in the input data.
Q8What are AVL trees? How are rotations used to maintain balance in AVL trees?
A8AVL trees are self-balancing binary search trees where the heights of the left and right subtrees differ by at most one. Rotations are used to maintain balance in AVL trees by reorganizing the tree structure based on certain conditions.
Q9What is a B-tree? How does it differ from a binary search tree?
A9A B-tree is a self-balancing search tree that can have multiple keys per node and multiple child nodes. It differs from a binary search tree by allowing a variable number of keys per node and supporting efficient disk-based operations.
Q10What is a B+ tree? How is it useful in database systems?
A10A B+ tree is a variation of a B-tree where only keys are stored in internal nodes, and data records are stored in the leaf nodes. It is useful in database systems for efficient range queries, sequential access, and indexing.

Module 5: Graphs

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Q1What is a graph? Explain its terminologies.
A1A graph is a nonlinear data structure consisting of a set of nodes (vertices) and a set of edges. Terminologies include vertices, edges, degree, adjacency, path, and cycle.
Q2How can a graph be represented?
A2A graph can be represented using various methods, such as an adjacency matrix, an adjacency list, or an edge list.
Q3What are the common graph traversal methods?
A3Common graph traversal methods include Depth-First Search (DFS) and Breadth-First Search (BFS).
Q4Explain the topological sorting of a directed acyclic graph (DAG).
A4Topological sorting of a DAG is an ordering of its vertices such that for every directed edge (u, v), vertex u comes before v in the ordering. It is useful in scheduling tasks with dependencies.
Q5What are the applications of graphs?
A5Graphs have applications in various domains, including social networks, computer networks, routing algorithms, recommendation systems, and optimization problems.
Q6How can you detect cycles in a graph?
A6Cycles in a graph can be detected using algorithms such as Depth-First Search (DFS) or by applying cycle detection algorithms like Tarjan’s algorithm or Kosaraju’s algorithm.
Q7What is a minimum spanning tree (MST)?
A7A minimum spanning tree is a tree that spans all the vertices of a connected, weighted graph with the minimum total weight. It is useful in network design and optimizing costs.
Q8How can you find the shortest path between two vertices in a graph?
A8Shortest paths between two vertices in a graph can be found using algorithms such as Dijkstra’s algorithm or the Bellman-Ford algorithm.
Q9Explain the concept of a directed graph and an undirected graph.
A9In a directed graph, edges have a direction, allowing traversal in only one direction. In an undirected graph, edges have no direction, allowing traversal in both directions.
Q10What is the difference between a graph and a tree?
A10A graph is a collection of nodes connected by edges, whereas a tree is a specific type of graph with no cycles and a hierarchical structure. Trees are acyclic graphs.

Module 6: Searching Techniques

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Q1What is linear search? How does it work?
A1Linear search is a simple searching algorithm that iterates through a collection until the target element is found or the end of the collection is reached. It compares each element sequentially to the target element.
Q2Explain the concept of binary search. How is it more efficient than linear search?
A2Binary search is a search algorithm used on sorted collections. It repeatedly divides the search space in half by comparing the target element with the middle element of the collection. It is more efficient than linear search as it eliminates half of the search space at each step.
Q3What is hashing? How does it work?
A3Hashing is a technique to map data to a fixed-size array index using a hash function. It computes an index based on the data’s characteristics, allowing for efficient retrieval and storage.
Q4What are collision resolution techniques in hashing?
A4Collision resolution techniques are used to handle situations where two elements map to the same index in hashing. Some techniques include separate chaining (using linked lists or other data structures) and open addressing (probing methods like linear probing, quadratic probing, or double hashing).
Q5What is a hash function? What are the properties of a good hash function?
A5A hash function is a function that takes an input (or key) and computes a fixed-size output (or hash value). Properties of a good hash function include uniform distribution, determinism, and minimal collision rate.
Q6Explain linear probing as a collision resolution technique in hashing.
A6Linear probing is a collision resolution technique where, if a collision occurs, the next available slot in the hash table is searched sequentially. It follows a linear sequence until an empty slot is found.
Q7What is binary probing in hashing?
A7Binary probing is a collision resolution technique in hashing where, if a collision occurs, the next slot to check is determined by a binary sequence of increments. It provides a more scattered search pattern compared to linear probing.
Q8What is quadratic probing? How does it handle collisions in hashing?
A8Quadratic probing is a collision resolution technique in hashing where, if a collision occurs, the next slot to check is determined by a quadratic sequence of increments. It provides a more distributed search pattern and helps reduce clustering.
Q9What is double hashing? How does it resolve collisions in hashing?
A9Double hashing is a collision resolution technique in hashing where, if a collision occurs, a secondary hash function is used to calculate the number of steps for the next slot to check. It helps in achieving a more uniform distribution of elements and reduces clustering.
Q10Compare the time complexity of linear search, binary search, and hashing for search operations.
A10The time complexity of linear search is O(n) in the worst case, binary search is O(log n) in the worst case, and hashing provides O(1) average case time complexity for search operations, making it the most efficient among the three for large data sets.

Conclusion: The viva questions and answers provided above cover various important topics in the field of data structures. By going through these questions, students can enhance their understanding of data structures and their practical applications. The questions cover modules such as Introduction to Data Structures, Stack and Queues, Linked List, Trees, Graphs, and Searching Techniques. Each module delves into specific subtopics and provides comprehensive explanations along with examples.

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