- Data Structures & Algorithms
- DSA - Home
- DSA - Overview
- DSA - Environment Setup
- DSA - Algorithms Basics
- DSA - Asymptotic Analysis
- Data Structures
- DSA - Data Structure Basics
- DSA - Data Structures and Types
- DSA - Array Data Structure
- Linked Lists
- DSA - Linked List Data Structure
- DSA - Doubly Linked List Data Structure
- DSA - Circular Linked List Data Structure
- Stack & Queue
- DSA - Stack Data Structure
- DSA - Expression Parsing
- DSA - Queue Data Structure
- Searching Algorithms
- DSA - Searching Algorithms
- DSA - Linear Search Algorithm
- DSA - Binary Search Algorithm
- DSA - Interpolation Search
- DSA - Jump Search Algorithm
- DSA - Exponential Search
- DSA - Fibonacci Search
- DSA - Sublist Search
- DSA - Hash Table
- Sorting Algorithms
- DSA - Sorting Algorithms
- DSA - Bubble Sort Algorithm
- DSA - Insertion Sort Algorithm
- DSA - Selection Sort Algorithm
- DSA - Merge Sort Algorithm
- DSA - Shell Sort Algorithm
- DSA - Heap Sort
- DSA - Bucket Sort Algorithm
- DSA - Counting Sort Algorithm
- DSA - Radix Sort Algorithm
- DSA - Quick Sort Algorithm
- Graph Data Structure
- DSA - Graph Data Structure
- DSA - Depth First Traversal
- DSA - Breadth First Traversal
- DSA - Spanning Tree
- Tree Data Structure
- DSA - Tree Data Structure
- DSA - Tree Traversal
- DSA - Binary Search Tree
- DSA - AVL Tree
- DSA - Red Black Trees
- DSA - B Trees
- DSA - B+ Trees
- DSA - Splay Trees
- DSA - Tries
- DSA - Heap Data Structure
- Recursion
- DSA - Recursion Algorithms
- DSA - Tower of Hanoi Using Recursion
- DSA - Fibonacci Series Using Recursion
- Divide and Conquer
- DSA - Divide and Conquer
- DSA - Max-Min Problem
- DSA - Strassen's Matrix Multiplication
- DSA - Karatsuba Algorithm
- Greedy Algorithms
- DSA - Greedy Algorithms
- DSA - Travelling Salesman Problem (Greedy Approach)
- DSA - Prim's Minimal Spanning Tree
- DSA - Kruskal's Minimal Spanning Tree
- DSA - Dijkstra's Shortest Path Algorithm
- DSA - Map Colouring Algorithm
- DSA - Fractional Knapsack Problem
- DSA - Job Sequencing with Deadline
- DSA - Optimal Merge Pattern Algorithm
- Dynamic Programming
- DSA - Dynamic Programming
- DSA - Matrix Chain Multiplication
- DSA - Floyd Warshall Algorithm
- DSA - 0-1 Knapsack Problem
- DSA - Longest Common Subsequence Algorithm
- DSA - Travelling Salesman Problem (Dynamic Approach)
- Approximation Algorithms
- DSA - Approximation Algorithms
- DSA - Vertex Cover Algorithm
- DSA - Set Cover Problem
- DSA - Travelling Salesman Problem (Approximation Approach)
- Randomized Algorithms
- DSA - Randomized Algorithms
- DSA - Randomized Quick Sort Algorithm
- DSA - Karger’s Minimum Cut Algorithm
- DSA - Fisher-Yates Shuffle Algorithm
- DSA Useful Resources
- DSA - Questions and Answers
- DSA - Quick Guide
- DSA - Useful Resources
- DSA - Discussion
Data Structures Algorithms Mock Test
This section presents you various set of Mock Tests related to Data Structures Algorithms. You can download these sample mock tests at your local machine and solve offline at your convenience. Every mock test is supplied with a mock test key to let you verify the final score and grade yourself.
Data Structures Algorithms Mock Test III
Q 1 - What will be the running-time of Dijkstra's single source shortest path algorithm, if the graph G(V,E) is stored in form of adjacency list and binary heap is used −
Answer : C
Explanation
The runing time will be Ο(|E|+|V| log |V|) when we use adjacency list and binary heap.
Q 2 - How many swaps are required to sort the given array using bubble sort - { 2, 5, 1, 3, 4}
Answer : A
Explanation
There will be 3 swaps in first iteration and 1 swap in second iteration.
Q 3 - Match the following −
| (1) Bubble Sort | (A) Ο(n) |
| (2) Shell Sort | (B) Ο(n2) |
| (3) Selection Sort | (C) Ο(n log n) |
Answer : B
Explanation
Q 4 - In context with time-complexity, find the odd out −
A - Deletion from Linked List.
Answer : D
Explanation
Binary heap heapify operation has time-complexity of Ο(n), while other operations have time-complexity of Ο(1) only.
Q 5 - In binary heap, whenever the root is removed then the rightmost element of last level is replaced by the root. Why?
A - It is the easiest possible way.
B - To make sure that it is still complete binary tree.
Answer : B
Explanation
A binary heap (whether max or min) has to satisfy the property of complete binary tree at all times.
Q 6 - Time required to merge two sorted lists of size m and n, is
Answer : B
Explanation
The time required to merge two sorted list is Ο(m + n).
Q 7 - The number of binary trees with 3 nodes which when traversed in post order gives the sequence A,B,C is ?
Answer : C
Explanation
Five binary trees (of 3 nodes) are possible.
Q 8 - Quick sort running time depends on the selection of
Answer : B
Explanation
If the pivot element is balanced, quick sort running time will be less.
Q 9 - Which of the below given sorting techniques has highest best-case runtime complexity −
Answer : B
Explanation
Selection sort best case time complexity is Ο(n2)
Q 10 - Which of the below mentioned sorting algorithms are not stable?
Answer : A
Explanation
Except selection sort, all other soring algorithms are stable.
Q 11 - If queue is implemented using arrays, what would be the worst run time complexity of queue and dequeue operations?
Answer : D
Explanation
As queue is maintained by two separate pointers for queue and dequeue operations, the run time for both is Ο(1).
Q 12 - A queue data-structure can be used for −
Answer : C
Explanation
Queues can be used for limited resource allocation. For other operations, stacks are used.
Q 13 - The Θ notation in asymptotic evaluation represents −
Answer : A
Explanation
Θ represents average case. Ο represents worst case and Ω represents base case.
Q 14 - Which of these alogrithmic approach tries to achieve localized optimum solution −
Answer : A
Explanation
Greedy approach focuses only on localized optimum solution.
Q 15 - Which of the following uses memoization?
B - Divide and conquer approach
Answer : C
Explanation
Remembering the results of previously calculated solutions is called memoization.
Q 16 - Index of arrays in C programming langauge starts from
Answer : A
Explanation
Arrays, in C, starts from 0 which is mapped to its base address.
Q 17 - In doubly linked lists
A - a pointer is maintained to store both next and previous nodes.
B - two pointers are maintained to store next and previous nodes.
Answer : B
Explanation
One pointer variable can not store more than one address values.
Q 18 - node.next -> node.next.next; will make
Answer : A
Explanation
After applying node.next -> node.next.next; we will not have node.next stored anywhere if not explicitly mentioned.
Answer : B
Explanation
Linked lists has search complexity of Ο(n).
Q 20 - Which of the following is not possible with an array in C programming langauge −
Answer : C
Explanation
Array in C are static and cannot be shrinked or expanded in run-time.
Q 21 - In C programming, when we remove an item from bottom of the stack, then −
Answer : B
Explanation
Stack can only be accessed from top of it.
Q 22 - Program with highest run-time complexity is
Answer : A
Explanation
Tower of hanoi has the highest run time complexity
Q 23 - Tower of hanoi is a classic example of
Answer : D
Explanation
The recursive approach of tower of hanoi uses divide and conquer method.
Q 24 - Which of the following algorithm cannot be desiged without recursion −
Answer : D
Explanation
Every problem which can be solved using recursion can also be solved using iterations.
Q 25 - If there's no base criteria in a recursive program, the program will
Answer : C
Explanation
Without a base criteria and progressive approach, a recursion is just an infinite iteration.
Answer Sheet
| Question Number | Answer Key |
|---|---|
| 1 | C |
| 2 | A |
| 3 | B |
| 4 | D |
| 5 | B |
| 6 | B |
| 7 | C |
| 8 | B |
| 9 | B |
| 10 | A |
| 11 | D |
| 12 | C |
| 13 | A |
| 14 | A |
| 15 | C |
| 16 | A |
| 17 | B |
| 18 | A |
| 19 | B |
| 20 | C |
| 21 | B |
| 22 | A |
| 23 | D |
| 24 | D |
| 25 | C |