- 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 II
Q 1 - Quick sort algorithm is an example of
Answer : D
Explanation
Quick sort divides the list using pivot and then sorts in recursive manner. It uses divide and conquer approach.
Q 2 - Which of the following asymptotic notation is the worst among all?
Answer : B
Explanation
Ο(n+9378) is n dependent
Ο(n3) is cubic
nΟ(1) is polynomial
2Ο(n) is exponential
Q 3 - The following formular is of
left_subtree (keys) ≤ node (key) ≤ right_subtree (keys)
Answer : C
Explanation
A binary search tree (BST) is a tree in which all nodes follows the below mentioned properties −
The left sub-tree of a node has key less than or equal to its parent node's key.
The right sub-tree of a node has key greater than or equal to its parent node's key.
Q 4 - Travelling salesman problem is an example of
Answer : B
Explanation
Travelling salesman is an example of greedy algorithm. Greedy algorithms tries to find localized optimum solution which may eventually land in globally optimized solutions.
Q 5 - Find the odd out
A - Prim's Minimal Spanning Tree Algorithm
B - Kruskal's Minimal Spanning Tree Algorithm
Answer : C
Explanation
Floyd-Warshall's All pair shortest path Algorithm uses dynamic programming approach. All other mentioned algorithms use greedy programming approach
Q 6 - Which of the following searching techniques do not require the data to be in sorted form
Answer : A
Explanation
Both binary and interpolation search requires data set to be in sorted form. Linear search can work even if the data is not sorted.
Answer : C
Explanation
Every connected graph at least has one spanning tree.
Q 8 - Visiting root node after visiting left and right sub-trees is called
Answer : C
Explanation
In Post-order traversal method, the root node is visited last, hence the name.
Q 9 - Binary search tree has best case run-time complexity of Ο(log n). What could the worst case?
Answer : A
Explanation
In case where binary search tree is left or right intended, the worst case can be Ο(n)
Q 10 - The minimum number of edges required to create a cyclid graph of n vertices is
Answer : A
Explanation
To make a graph cyclic, the number of edges should be at least equal to vertices in the graph.
Q 11 - Maximum degree of any vertex in a simple graph of vertices n is
Answer : D
Explanation
In a simple graph, a vertex can have edge to maximum n - 1 vertices.
Q 12 - What could be the worst case height of an AVL tree?
Answer : C
Explanation
Worst case height of an AVL tree is 1.44 log n
Q 13 - What is not true about insertion sort?
A - Exhibits the worst case performance when the initial array is sorted in reverse order.
B - Worst case and average case performance is Ο(n2)
C - Can be compared to the way a card player arranges his card from a card deck.
Answer : D
Explanation
All given options are true about insertion sort.
Q 14 - Which of the following algorithm is not stable?
Answer : B
Explanation
Among the given, only quick sort is not stable that is it may re-arrange the already sorted items.
Q 15 - If the array is already sorted, which of these algorithms will exhibit the best performance
Answer : B
Explanation
Insertion sort, as it should then work in linear way.
Q 16 - Which of the following is example of in-place algorithm?
Answer : B
Explanation
Only Merge sort requires extra space.
Q 17 - Graph traversal is different from a tree traversal, because
Answer : C
Explanation
As trees do not have loops, they are easier to traverse.
Q 18 - Which method can find if two vertices x & y have path between them?
Answer : C
Explanation
By using both BFS and DFS, a path between two vertices of a connected graph can be determined.
Q 19 - Time complexity of Depth First Traversal of is
Answer : A
Explanation
Using Depth First Search, we traverse the whole graph i.e. visiting all Vertices and Edges.
Q 20 - An algorithm is
A - a piece of code to be executed.
B - a loosely written code to make final code.
Answer : C
Explanation
An algorithm is a step by step procedure to solve a computer problem.
Q 21 - Apriory algorithm analysis does not include −
Answer : C
Explanation
Algorithms are independent of programming languages, hence, program complexity is not a part of algorithm analysis.
Q 22 - Which of the below given series is Non-Increasing Order −
Answer : C
Explanation
A sequence of values is said to be in non-increasing order, if the successive element is less than or equal to its previous element in the sequence.
Q 23 - Which of the following has search effeciency of Ο(1) −
Answer : C
Explanation
A simple hash table has the Ω(1) efficiency.
Q 24 - After each iteration in bubble sort
A - at least one element is at its sorted position.
Answer : A
Explanation
In one iteration of Bubble sort, the maximum of the set in hand is moved at the end of the unsorted list. Hence one less comparison.
Q 25 - What about recursion is true in comparison with iteration?
A - very expensive in terms of memory.
C - every recursive program can be written with iteration too.
Answer : D
Explanation
Recursion is just an other way to write the same program code. But calling a function again and again makes it expensive in terms of memory, CPU cycles and delivers less performance.
Answer Sheet
| Question Number | Answer Key |
|---|---|
| 1 | D |
| 2 | B |
| 3 | C |
| 4 | B |
| 5 | C |
| 6 | A |
| 7 | C |
| 8 | C |
| 9 | A |
| 10 | A |
| 11 | D |
| 12 | C |
| 13 | D |
| 14 | B |
| 15 | B |
| 16 | B |
| 17 | C |
| 18 | C |
| 19 | A |
| 20 | C |
| 21 | C |
| 22 | C |
| 23 | C |
| 24 | A |
| 25 | D |