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1. ... is very uselul in situations when diata have to be stored asd the retrieve in the reverse order (a) Stack (b) Quieue (c) Link hist (d) Array 2. Insering an item into the stack, when stack is not cmpty is callod operation and deletion of item from stack is not enupty is called (3) Push, pop (b) Rop, push (c) Insert delcte (d) Delete, insert 3. Which or the following isa nomlinear data structur (i) Stack (b) Eist (c) String (d) Tress +. The simplest typo of datu structures is (a) Muhi- dimersional array (brtincar array (c) Two-dimensional array (d) threc-dimensional array 5. ininc... is a pile in which items ane added af one eod and remo irm the other (a) Stack (b) Queue (d) Keys 16. Each node in a graph data structure is tepresented by a?" (a) Edge (b) Adjacent (c) Path (d) Vertex 17. Which of the following atgerithm is quite effective for lurge size data sets (a) Quick sart (b) Selection sort (c) Bubble sart (d) Insertion sort 18. The technique that converts a range of key values into a range of indexes of an arriy is called? (a) Algorithm (b) Hashing (c) Soring (d) Selection 19. All the following are characteristics of data structures except (a) Correctness (b) Time complexity (c) Entity set (d) Space complexity 20. The complexity of an algonthm is dependent on and (a) Input and output (b) Theta and omega notations (c) Black box and white box testing (d) Time factor and space factor 2. (a) What is asymptotic analysis? (b) Explain the following termis in data struetures: i. Time complexily if Worse case iii. Average case iv. Space complexity 3. (a) What is Traversal (2. M.AKS) (b) What is binary search tree (2.MARKS) (c) Explain how the elements in the tree can be traverse in the followin methods i) In-order Imenal ii) Pre-order Iraversil iii) Post-order Traversal 4. (a) Explain the following i. Big O Notation ii. Omega Natation iii. Theta Notation (9 MARKS) (b) State two problems that can be solved using data structures (1 MARK) 5. (a) Explain the following terms i) Greedy algorithm ii) Divide and conquer iii) Dynamic programming (9 MARKS) (b) State two problems that current technology applications faces? (I MARK) SECHON B (40 MARKS) WSWER QUESTION ONE (1) AND ANY OTHER TWO (2) (a) What is data structures (2. MARES) (b) Ait arithunetic expression can be written in three diffenent bet chuivalent notations, Copy and complete the table befow sorted using the following. i. Bubble sort ii. Selection sort iii. Insertion sor (d) What is Depth First Search algorithm and show how the follongat at will be stored in-stack bs.

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Data Structures Multiple Choice Questions (MCQs) are designed to test an individual's understanding of the fundamental concepts, algorithms, and implementations related to organizing and storing data efficiently. These questions cover a wide range of topics within the field of data structures. Below is a description outlining the common themes addressed by MCQs in this domain: Basic Concepts: MCQs may cover fundamental concepts such as arrays, linked lists, stacks, and queues. Understanding the basic properties, advantages, and disadvantages of these data structures is often assessed. Trees and Graphs: Questions may focus on tree structures, including binary trees, AVL trees, and B-trees. Graph-related topics could include concepts like traversal algorithms, connectivity, and properties of directed and undirected graphs. Hashing: MCQs often evaluate knowledge of hashing techniques and collision resolution methods. Understanding the principles of hash functions and their applications in data retrieval is essential. Sorting and Searching: Questions may cover various sorting algorithms (e.g., bubble sort, quicksort, mergesort) and their time complexities. Searching algorithms, such as binary search and linear search, might be included in these questions. Dynamic Programming: MCQs may assess the understanding of dynamic programming concepts, including optimal substructure and overlapping subproblems. Knowledge of dynamic programming techniques to solve problems efficiently may be evaluated. Graph Algorithms: Questions may explore graph traversal algorithms like depth-first search (DFS) and breadth-first search (BFS). Shortest path algorithms (e.g., Dijkstra's algorithm, Bellman-Ford algorithm) and minimum spanning tree algorithms may also be covered. Heap and Priority Queue: MCQs may inquire about heap data structures and their applications, including priority queues. Understanding the operations and time complexities associated with heaps is commonly tested. Advanced Data Structures: Questions may cover more advanced data structures such as trie, segment tree, and Fenwick tree (Binary Indexed Tree). Knowledge of these structures and their use cases is often examined in MCQs. Abstract Data Types (ADTs): MCQs may assess the understanding of abstract data types like stacks, queues, and sets. Candidates may be tested on their ability to choose appropriate ADTs based on specific requirements. Space and Time Complexity Analysis: Questions may evaluate the ability to analyze the time and space complexity of algorithms and data structures. Candidates may need to compare different algorithms and choose the most efficient one for a given problem. Applications and Real-World Scenarios: Some MCQs may present real-world scenarios and ask candidates to identify the most suitable data structure or algorithm for solving specific problems. These MCQs aim to assess a candidate's proficiency in understanding, implementing, and analyzing various data.

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