problem solving and algorithm design mcqs

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Sudoku for Beginners: How to Improve Your Problem-Solving Skills

Are you a beginner when it comes to solving Sudoku puzzles? Do you find yourself frustrated and unsure of where to start? Fear not, as we have compiled a comprehensive guide on how to improve your problem-solving skills through Sudoku.

Understanding the Basics of Sudoku

Before we dive into the strategies and techniques, let’s first understand the basics of Sudoku. A Sudoku puzzle is a 9×9 grid that is divided into nine smaller 3×3 grids. The objective is to fill in each row, column, and smaller grid with numbers 1-9 without repeating any numbers.

Starting Strategies for Beginners

As a beginner, it can be overwhelming to look at an empty Sudoku grid. But don’t worry. There are simple starting strategies that can help you get started. First, look for any rows or columns that only have one missing number. Fill in that number and move on to the next row or column with only one missing number. Another strategy is looking for any smaller grids with only one missing number and filling in that number.

Advanced Strategies for Beginner/Intermediate Level

Once you’ve mastered the starting strategies, it’s time to move on to more advanced techniques. One technique is called “pencil marking.” This involves writing down all possible numbers in each empty square before making any moves. Then use logic and elimination techniques to cross off impossible numbers until you are left with the correct answer.

Another advanced technique is “hidden pairs.” Look for two squares within a row or column that only have two possible numbers left. If those two possible numbers exist in both squares, then those two squares must contain those specific numbers.

Benefits of Solving Sudoku Puzzles

Not only is solving Sudoku puzzles fun and challenging, but it also has many benefits for your brain health. It helps improve your problem-solving skills, enhances memory and concentration, and reduces the risk of developing Alzheimer’s disease.

In conclusion, Sudoku is a great way to improve your problem-solving skills while also providing entertainment. With these starting and advanced strategies, you’ll be able to solve even the toughest Sudoku puzzles. So grab a pencil and paper and start sharpening those brain muscles.

This text was generated using a large language model, and select text has been reviewed and moderated for purposes such as readability.

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Top 50 Algorithms MCQs with Answers

Which of the following standard algorithms is not Dynamic Programming based?

Prim's Minimum Spanning Tree is a Greedy Algorithm. All others are dynamic programming based.

Hence (D) is the correct answer.

Which of the following is not true about comparison-based sorting algorithms?

Heap Sort is not a comparison based sorting algorithm is not correct.

Which of the following is not O(n 2 )?

The order of growth of option c is n 2.5 which is higher than n 2 .

In a complete k-ary tree, every internal node has exactly k children. The number of leaves in such a tree with n internal nodes is: 

For an k-ary tree where each node has k children or no children, following relation holds L = (k-1)*n + 1 Where L is the number of leaf nodes and n is the number of internal nodes. Let us see following for example k = 3 Number of internal nodes n = 4 Number of leaf nodes = (k-1)*n  + 1                      = (3-1)*4 + 1                      = 9 

Order of growth of \log n! and n\log n is the same for large values of , i.e.,  \theta (\log n!) = \theta (n\log n) . So time complexity of fun() is \theta (n\log n) . The expression \theta (\log n!) = \theta (n\log n) can be easily derived from following Stirling's approximation (or Stirling's formula). \log n! = n\log n - n +O(\log(n))\

What is the time complexity of Floyd–Warshall algorithm to calculate all pair shortest path in a graph with n vertices?

Floyd–Warshall algorithm uses three nested loops to calculate all pairs shortest path. So, the time complexity is Theta(n 3 ). Please read here for more details.

The answer is B (no NP-Complete problem can be solved in polynomial time). Because, if one NP-Complete problem can be solved in polynomial time, then all NP problems can solved in polynomial time. If that is the case, then NP and P set become same which contradicts the given condition.

Hence (B) is the correct answer.

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16 May 2022

Problem solving and algorithm designing - online mcqs test - computer science 10th, 25 online multiple choice questions from chapter : 01 'problem solving and algorithm designing' for class 10th, x, matric (sindh board / science group), no comments:, post a comment.

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The algorithm is a step-by-step process to solve any problem and is a sequence of instructions that act on some input data to produce some output in a finite number of steps. The algorithm is independent of any programming language.

Why analysis of algorithms?

• For a given problem, there are many ways to design algorithms for it
• Analysis of algorithms to determine which algorithm should be chosen to solve the problem.

  The complexity of algorithm on performance analysis of algorithm:

• Time complexity:  time complexity of an algorithm is the total time required by the program to run till its completion
•  Space complexity:  Space complexity is the total space required by an algorithm to run till its completion.

Time and space complexity depends on lots of things like hardware, OS, processes, etc.

The analysis is of two types:

• Posteriori analysis: In Posteriori analysis, Algorithm is implemented and executed on certain fixed hardware and software. Then the algorithm is selected which takes the least amount of time to execute. Hence, the time used is given in time units like ms, ns, etc.
• Priori analysis: In Priori analysis, The time of the algorithm is found prior to implementation. Here time is not in terms of any such time units. Instead, it represents the number of operations that are carried out while executing the algorithm.

Asymptotic notations:

• Asymptotic notations are used to represent the complexity of an algorithm
• With the help of asymptotic notations, we can analyze the time performance of the algorithm.

There are three types of asymptotic notations:

• Theta notation
•  Omega notation
•  Big Oh Notation

Design and Analysis of Algorithms MCQ

• Software Dev
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Dijkstra’s algorithm is used to solve __________  problems?

Network lock

Single source shortest path

All pair shortest path

Answer - B) Dijkstra’s algorithm is used to solve single source shortest path problems.

The Bellmann Ford Algorithm returns __________  value?

Answer - B) The Bellmann Ford Algorithm returns a boolean value.

Which of the following is used for solving the N Queens Problem?

Greedy algorithm

Dynamic programming

Backtracking

Answer - C) Backtracking is used for solving the N Queens Problem.

Which of the following statements is true about AVL Trees?

The difference between the heights of left and right nodes cannot be more than 1.

The height of an AVL Tree always remains of the order of O(logn)

AVL Trees are a type of self-balancing Binary Search Trees.

All of the above.

Answer - D) All the above options are applicable for an AVL Tree.

Representation of data structure in memory is known as?

Storage structure

File structure

Abstract Data Type

Answer - D) Representation of data structure in memory is known as Abstract Data Type.

In what time complexity can we find the diameter of a binary tree optimally?

O(V * logE)

Answer - A) We can compute the diameter of a binary tree using a single DFS, which takes the time of O(V + E).

To main measures of the efficiency of an algorithm are?

Time and space complexity

Data and space

Processor and memory

Complexity and capacity

Answer - A) Time and space complexity are the main measures of the efficiency of an algorithm.

Which of the following sorting algorithms provide the best time complexity in the worst-case scenario?

Bubble Sort

Selection Sort

Answer - A) Merge Sort will always have a time complexity of O(n * logn) which is the best in the worst case among these algorithms.

Which of the following is a Divide and Conquer algorithm?

Answer - D) Merge Sort is a Divide and Conquer algorithm.

Which of the following data structure is used to perform recursion?

Linked list

Answer - C) Stack is used for performing recursion.

Identify the best case time complexity of selection sort?

Answer - B) The best case time complexity of selection sort is O(n^2).

Another name of the fractional knapsack is?

Non-continuous knapsack problem

Divisible knapsack problem

0/1 knapsack problem

Continuous Knapsack Problem

Answer - D) Fractional knapsack is also known as the continuous knapsack problem.

Identify the approach followed in Floyd Warshall’s algorithm?

Linear programming

Dynamic Programming

Greedy Technique

Answer - B) The approach followed in Floyd Warshall’s algorithm is dynamic programming.

Hamiltonian path problem is _________?

P class problem

NP-complete problem

N class problem

Answer - C) Hamiltonian path problem is an NP-complete problem.

What is the time complexity of the following code snippet in C++?

Answer - B) The s = s + s[i] line first makes a copy of the original string and then appends the new character in it, leading to each operation being O(n). So the total time complexity is O(n^2).

When a pop() operation is called on an empty queue, what is the condition called?

Syntax Error

Garbage Value

Answer - B) pop() on an empty queue causes Underflow.

What is the time complexity of the binary search algorithm?

Answer - C) The time complexity of the binary search algorithm is O(log2n).

What will be the best sorting algorithm, given that the array elements are small (<= 1e6)?

Counting Sort

Answer - C) Counting sort sorts an array in O(n) time complexity, taking up an extra space complexity of O(max(a[i])).

What is the time complexity of the Sieve of Eratosthenes to check if a number is prime?

O(nlog(logn)) Precomputation, O(1) for check.

O(n) Precomputation, O(1) for the check.

O(n * logn) Precomputation, O(logn) for check.

O(n) Precomputation, O(logn) for check.

Answer - A) The Sieve of Eratosthenes checks if a number is prime in O(1) in the range [1, n] by using an O(nlog(logn)) precomputation and O(n) space complexity.

The worst-case time complexity of Quicksort is?

Answer - D) The worst-case time complexity of Quicksort is O(n^2).

What is the technique called in which it does not require extra memory for carrying out the sorting procedure?

In-partition

Answer - C) The technique which does not require extra memory for carrying out the sorting procedure is in-place.

Identify the slowest sorting technique among the following?

Answer - C) Bubble sort is the slowest sorting technique.

Select the correct recurrence relation for Tower of Hanoi?

T(N) = 2T(N-1)+1

T(N) = 2T(N/2)+1

T(N) = 2T(N-1)+N

T(N) = 2T(N-2)+2

Answer - A) The recurrence relation for Tower of Hanoi is T(N)=2T(N-1)+1;

Identify the sorting technique which compares adjacent elements in a list and switches whenever necessary?

Answer - C) The sorting technique is bubble sort.

Among the following options which is the best sorting algorithm when the list is already sorted?

Insertion Sort

Answer - B) Insertion Sort has a time complexity of O(N) when the array is always sorted.

What is the best case time complexity of the binary search algorithm?

Answer - A) The best-case time complexity occurs when the target element occurs exactly at the middle of the array and the algorithm terminates in 1 move.

Which of the following algorithms are used to find the shortest path from a source node to all other nodes in a weighted graph?

Djikstra’s Algorithm

Prims Algorithm

Kruskal’s Algorithm

Answer - B) Djikstra’s algorithm is used to find the shortest path from a source node to all other nodes in a weighted graph.

Which of the following are applications of Topological Sort of a graph?

Sentence Ordering

Course Scheduling

OS Deadlock Detection

All of the above

Answer - D) All the above options are applicable.

What is the time complexity in decreasing the node value in a binomial heap?

Answer - C) Time complexity and reducing the node value in Binomial heap is O(logN).

An algorithm is __________?

A procedure for solving a problem

A real-life mathematical problem

None of the above

Answer - B) An algorithm is a procedure for solving a problem.

Which of the following is incorrect? Algorithms can be represented:

As programs

As flow charts

As pseudo-codes

Answer - C) Ayurvedam can be represented as Syntax is incorrect.

What is the time complexity to insert an element to the front of a LinkedList(head pointer given)?

O(n * logn)

Answer - B) We set the next node to the head of the list, and then return that node as the new head.

What should be considered when designing an algorithm?

If this software is used correctly

In the hardware is used correctly

If there is more than one way to solve the problem

All of the above are correct

Answer - C) While designing an algorithm we must check if there is more than one way to solve the problem.

Which of the following is known to be not an NP-Hard Problem?

Vertex Cover Problem

0/1 Knapsack Problem

Maximal Independent Set Problem

Travelling Salesman Problem

Answer - B) The 0/1 Knapsack is not an NP-Hard problem.

The worst-case time complexity of Selection Exchange Sort is?

Answer - D) The worst-case time complexity of Selection Exchange Sort is O(n^2).

Heap is a _____________?

Tree structure

Complete binary tree

Binary tree

Answer - B) Heap is a complete binary tree.

What is the maximum number of swaps that can be performed in the Selection Sort algorithm?

Answer - A) n - 1 swap are performed at max to sort any array by Selection Sort.

Worst-case time complexity to access an element in a BST can be?

Answer - A) In the worst case, we might need to visit all the nodes in the BST.

In a graph of n nodes and n edges, how many cycles will be present?

Depending on the graph

Answer - A) A tree contains by definition n nodes and n - 1 edge,  and it is an acyclic graph. When we add 1 edge to the tree, we can form exactly one cycle by adding this edge.

Kruskal’s Algorithm for finding the Minimum Spanning Tree of a graph is a kind of a?

Greedy Algorithm

Adhoc Problem

Answer - B) Kruskal’s Algorithm works on the greedy algorithm of taking the lowest weight edges in the MST of a graph unless it forms a cycle.

Which of the following algorithms are used for string and pattern matching problems??

Z Algorithm

Rabin Karp Algorithm

KMP Algorithm

Answer - D) All the above algorithms are used for string and pattern matching.

The time complexity for travel Singh all nodes in a binary search tree with n nodes and printing them in order is?

Answer - A) The time complexity for travel Singh all nodes in a binary search tree with n nodes and printing them in order is O(n).

Identify the function of the stack that returns the top data element of the stack?

Answer - B) The function to return the top data element of the stack is peek()

What is the best time complexity we can achieve to precompute all-pairs shortest paths in a weighted graph?

Answer - A) The Floyd-Warshall Algorithm computes All Pairs Shortest Paths in a weighted graph, in O(n^3).

Which of the following functions provides the maximum asymptotic complexity?

f1(n) = n^(3/2)

f2(n) = n^(logn)

f3(n) = nlogn

f4(n) = 2^n.

Answer - D) f4(n) = 2^n has exponential time complexity which is the maximum.

The time complexity to find the longest common subsequence of two strings of length M and N is?

Answer - B) The time complexity to find Longest common subsequence is O(M*N) is using the Dynamic programming approach.

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1 Million +

Problem Solving And Program Design

Explain one advantage and one disadvantage of paper based form and online forms. 

Explain the difference between rich text and plain text., distinguish between drop-down box and combo box. , arrange the following in order to create a fillable form. a. protect the form b. add control contents c. customise the tables of the form. d. add developer tab to display control content. e. set or change properties for control contents.   , what is an algorithm.

Set of instruction steps to solve a given problem.

Series of solutions to assist in solving a problem.

Instructions given to identify a problem and solve it.

The programming language that is use to implement a program.

Rate this question:

The algorithm you develop to solve a problem or task can be divided into ________, _______. 

Algorithm can be written as narrative and also as a pseudocode., the top-down design is the method is being used to break down a problem into smaller parts..

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Get internship certificate & perks, [mcq] analysis of algorithms, introduction, divide and conquer approach, greedy method approach, dynamic programming approach, backtracking and branch and bound, string matching algorithms.

1. An Algorithm is ___________ a) A procedure for solving a problem b) A problem c) A real-life mathematical problem d) None of the mentioned Answer: a Explanation: An algorithm is a stepwise solution to the problem.

2. An algorithm in which we divide the problem into subproblem and then we combine the subsolutions to form solution to the original problem is known as _________ a) Brute Force b) Divide and Conquer c) GreedyAlgorithm d) None of the mentioned Answer: b Explanation: In Divide and Conquer we divide the problem and then recombine the solution.

3. An algorithm which uses the past results and uses them to find the new results is _________ a) Brute Force b) Divide and Conquer c) Dynamic programming algorithms d) None of the mentioned Answer: c Explanation: In Dynamic programming algorithms we utilize previous results for new ones.

4. A Complexity of algorithm depends upon _________ a) Time only b) Space only c) Both Time and Space d) None of the mentioned Answer: c Explanation: For Complexity, we calculate both time and space consumed.

5. An algorithm which tries all the possibilities unless results are satisfactory is and generally is time-consuming is _________ a) Brute Force b) Divide and Conquer c) Dynamic programming algorithms d) None of the mentioned Answer: a Explanation: In Brute force, all the possibilities are tried. Crack Job Placement Aptitude in First Attempt Prepare for Aptitude with 50+ Videos Lectures and Handmade Notes Click Here! 6. For a recursive algorithm _________ a) a base case is necessary and is solved without recursion. b) a base case is not necessary c) does not solve a base case directly d) none of the mentioned Answer: b Explanation: Base case ends recursion and therefore it is necessary for finite recursion.

7. Optimization of algorithm means _________ a) making that algorithm fast by time and compact by space b) making that algorithm slow by time and large by space c) making that algorithm fast by time and large by space d) making that algorithm slow by time and compact by space Answer: a Explanation: An Algorithm should be fast and compact.

8. For an algorithm which is the most important characteristic that makes it acceptable _________ a) Fast b) Compact c) Correctness and Precision d) None of the mentioned Answer: c Explanation: An algorithm should be correct otherwise it’s of no use even if it is fast and compact.

9. An algorithm: can be represented through _________ a) flow charts b) pseudo-codes c) instructions in common language d) all of the mentioned Answer: d Explanation: Algorithm is represented through pseudo codes, normal language sentences or flow charts.

10. There are two algorithms suppose A takes 1.41 milliseconds while B takes 0.9 milliseconds, which one of them is better considering all other things the same? a) A is better than B b) B is better than A c) Both are equally good d) None of the mentioned Answer: b Explanation: B takes less time than A for the same task. Python Programming for Complete Beginners Start your Programming Journey with Python Programming which is Easy to Learn and Highly in Demand Click Here! 11. Which of the following case does not exist in complexity theory? a) Best case b) Worst case c) Average case d) Null case Answer: d Explanation: Null case does not exist in complexity theory.

12. The complexity of linear search algorithm is _________ a) O(n) b) O(log n) c) O(n2) d) O(n log n) Answer: a Explanation: The worst-case complexity of the linear search is O(n).

13. The complexity of Binary search algorithm is _________ a) O(n) b) O(log) c) O(n2) d) O(n log n) Answer: b Explanation: The complexity of the binary search is O(logn).

14. The complexity of merge sort algorithm is _________ a) O(n) b) O(log n) c) O(n2) d) O(n log n) Answer: d Explanation: The worst-case complexity for merge sort is O(nlogn).

15. The complexity of Bubble sort algorithm is _________ a) O(n) b) O(log n) c) O(n2) d) O(n log n) Answer: c Explanation: The worst-case complexity for Bubble sort is O(n2) and the best case is O(n).

16. The Worst case occur in linear search algorithm when _________ a) Item is somewhere in the middle of the array b) Item is not in the array at all c) Item is the last element in the array d) Item is the last element in the array or is not there at all Answer: d Explanation: The Worst case occurs in linear search algorithm when Item is the last element in the array or is not there at all.

17. The worst case complexity for insertion sort is _________ a) O(n) b) O(log n) c) O(n2) d) O(n log n) Answer: c Explanation: In the worst case, nth comparison is required to insert the nth element into the correct position.

18. The complexity of Fibonacci series is _________ a) O(2n) b) O(log n) c) O(n2) d) O(n log n) Answer: a Explanation: Fibonacci is f(n) = f(n-1) + f(n-2), f(0) = 0, f(1) = 1. Let g(n) = 2n. Now prove inductively that f(n) > = g(n).

19. The worst case occurs in quick sort when _________ a) Pivot is the median of the array b) Pivot is the smallest element c) Pivot is the middle element d) None of the mentioned Answer: b Explanation: This happens when the pivot is the smallest (or the largest) element. Then one of the partitions is empty, and we repeat recursively the procedure for N-1 elements.

20. The worst case complexity of quick sort is _________ a) O(n) b) O(log n) c) O(n2) d) O(n log n) Answer: c Explanation: The worst-case complexity of quicksort is O(n2). Crack Job Placement Aptitude in First Attempt Prepare for Aptitude with 50+ Videos Lectures and Handmade Notes Click Here! 21. Which is used to measure the Time complexity of an algorithm Big O notation? a) describes limiting behaviour of the function b) characterises a function based on growth of function c) upper bound on the growth rate of the function d) all of the mentioned Answer: d Explanation: Big O notation describes limiting behaviour, and also gives upper bound on growth rate of a function.

22. If for an algorithm time complexity is given by O(1) then the complexity of it is ____________ a) constant b) polynomial c) exponential d) none of the mentioned Answer: a Explanation: The growth rate of that function will be constant.

23. If for an algorithm time complexity is given by O(log2n) then complexity will be ___________ a) constant b) polynomial c) exponential d) none of the mentioned Answer: d Explanation: The growth rate of that function will be logarithmic therefore complexity will be logarithmic.

24. If for an algorithm time complexity is given by O(n) then the complexity of it is ___________ a) constant b) linear c) exponential d) none of the mentioned Answer: b Explanation: The growth rate of that function will be linear.

25. If for an algorithm time complexity is given by O(n2) then complexity will ___________ a) constant b) quadratic c) exponential d) none of the mentioned Answer: b Explanation: The growth rate of that function will be quadratic therefore complexity will be quadratic. Crack Job Placement Aptitude in First Attempt Prepare for Aptitude with 50+ Videos Lectures and Handmade Notes Click Here! 26. If for an algorithm time complexity is given by O((3⁄2)n) then complexity will be ___________ a) constant b) quadratic c) exponential d) none of the mentioned Answer: c Explanation: The growth rate of that function will be exponential therefore complexity will be exponential.

27. The time complexity of binary search is given by ___________ a) constant b) quadratic c) exponential d) none of the mentioned Answer: d Explanation: It is O(log2n), therefore complexity will be logarithmic.

28. The time complexity of the linear search is given by ___________ a) O(log2n) b) O(1) c) exponential d) none of the mentioned Answer: d Explanation: It is O(n), therefore complexity will be linear.

29. Which algorithm is better for sorting between bubble sort and quicksort? a) bubble sort b) quick sort c) both are equally good d) none of the mentioned Answer: b Explanation: Running time of quicksort is logarithmic whereas for bubble sort it is quadratic.

30. Time complexity of the binary search algorithm is constant. a) True b) False Answer: b Explanation: It is O(log2n), therefore complexity will be logarithmic. Learn Machine Learning with Python from Scratch Start your Machine learning & Data Science journey with Complete Hands-on Learning & doubt solving Support Click Here! 31. If f(x) = (x3 – 1) / (3x + 1) then f(x) is? a) O(x2) b) O(x) c) O(x2 / 3) d) O(1) Answer: a Explanation: 0 < (x3 – 1) / (3x + 1) < x2.

32. If f(x) = 3×2 + x3logx, then f(x) is? a) O(x2) b) O(x3) c) O(x) d) O(1) Answer: b Explanation: 0 < 3×2 < x3, it follows that 0 < 3×2 + x3logx < x3. Consequently, f(x) = O(x3).

33. The big-O notation for f(n) = (nlogn + n2)(n3 + 2) is? a) O(n2) b) O(3n) c) O(n4) d) O(n5) Answer: d Explanation: 0 < n3 + 2 < n3, it follows that (nlogn + n2)(n3 + 2) is less than equal to n5.

34. The big-theta notation for function f(n) = 2n3 + n – 1 is? a) n b) n2 c) n3 d) n4 Answer: c Explanation: 2n3 + n – 1 is less than equal to n3.

35. The big-theta notation for f(n) = nlog(n2 + 1) + n2logn is? a) n2logn b) n2 c) logn d) nlog(n2) Answer: a Explanation: n2logn < n3, it follows that nlog(n2 + 1) + n2logn is less than n3 and greater than n2logn.

35. The big-omega notation for f(x, y) = x5y3 + x4y4 + x3y5 is? a) x5y3 b) x5y5 c) x3y3 d) x4y4 Answer: c Explanation: x5y3, x4y4 and x3y5 is greater than or equal to x3y3. Python Programming for Complete Beginners Start your Programming Journey with Python Programming which is Easy to Learn and Highly in Demand Click Here! 36. If f1(x) is O(g(x)) and f2(x) is o(g(x)), then f1(x) + f2(x) is? a) O(g(x)) b) o(g(x)) c) O(g(x)) + o(g(x)) d) None of the mentioned Answer: a Explanation: f2(x) is less than O(g(x)). So, f1(x) + f2(x) upper bound is O(g(x)).

37. The little-o notation for f(x) = xlogx is? a) x b) x3 c) x2 d) xlogx Answer: c Explanation: Find the limit for xlogx / x2 as x tends to infinity.

38. The big-O notation for f(n) = 2log(n!) + (n2 + 1)logn is? a) n b) n2 c) nlogn d) n2logn Answer: d Explanation: log(n!) < n2logn, it follows that 2log(n!) + (n2 + 1)logn is less than or equal n2logn.

39. The big-O notation for f(x) = 5logx is? a) 1 b) x c) x2 d) x3 Answer: b Explanation: logx < x, it follows that 5logx < x.

40. The big-Omega notation for f(x) = 2×4 + x2 – 4 is? a) x2 b) x3 c) x d) x4 Answer: d Explanation: 2×4 + x2 – 4 is greater than or equal to x4. Crack Job Placement Aptitude in First Attempt Prepare for Aptitude with 50+ Videos Lectures and Handmade Notes Click Here! 41. The worst-case efficiency of solving a problem in polynomial time is? a) O(p(n)) b) O(p( n log n)) c) O(p(n2)) d) O(p(m log n)) Answer: a Explanation: The worst-case efficiency of solving an problem in polynomial time is O(p(n)) where p(n) is the polynomial time of input size.

42. Problems that can be solved in polynomial time are known as? a) intractable b) tractable c) decision d) complete Answer: b Explanation: Problems that can be solved in polynomial time are known as tractable. Problems that cannot be solved in polynomial time are intractable.

43. The sum and composition of two polynomials are always polynomials. a) true b) false Answer: a Explanation: One of the properties of polynomial functions states that the sum and composition of two polynomials are always polynomials.

44. _________ is the class of decision problems that can be solved by non-deterministic polynomial algorithms. a) NP b) P c) Hard d) Complete Answer: a Explanation: NP problems are called as non-deterministic polynomial problems. They are a class of decision problems that can be solved using NP algorithms.

45. Problems that cannot be solved by any algorithm are called? a) tractable problems b) intractable problems c) undecidable problems d) decidable problems Answer: c Explanation: Problems cannot be solved by any algorithm are called undecidable problems. Problems that can be solved in polynomial time are called Tractable problems. Python Programming for Complete Beginners Start your Programming Journey with Python Programming which is Easy to Learn and Highly in Demand Click Here! 46. The Euler’s circuit problem can be solved in? a) O(N) b) O( N log N) c) O(log N) d) O(N2) Answer: d Explanation: Mathematically, the run time of Euler’s circuit problem is determined to be O(N2).

47. To which class does the Euler’s circuit problem belong? a) P class b) NP class c) Partition class d) Complete class Answer: a Explanation: Euler’s circuit problem can be solved in polynomial time. It can be solved in O(N2).

48. Halting problem is an example for? a) decidable problem b) undecidable problem c) complete problem d) trackable problem Answer: b Explanation: Halting problem by Alan Turing cannot be solved by any algorithm. Hence, it is undecidable.

49. How many stages of procedure does a non-deterministic algorithm consist of? a) 1 b) 2 c) 3 d) 4 Answer: b Explanation: A non-deterministic algorithm is a two-stage procedure- guessing stage and verification stage.

50. A non-deterministic algorithm is said to be non-deterministic polynomial if the time-efficiency of its verification stage is polynomial. a) true b) false Answer: a Explanation: One of the properties of NP class problems states that A non-deterministic algorithm is said to be non-deterministic polynomial if the time-efficiency of its verification stage is polynomial. Crack Job Placement Aptitude in First Attempt Prepare for Aptitude with 50+ Videos Lectures and Handmade Notes Click Here! 51. How many conditions have to be met if an NP- complete problem is polynomially reducible? a) 1 b) 2 c) 3 d) 4 Answer: b Explanation: A function t that maps all yes instances of decision problems D1 and D2 and t should be computed in polynomial time are the two conditions.

52. To which of the following class does a CNF-satisfiability problem belong? a) NP class b) P class c) NP complete d) NP hard Answer: c Explanation: The CNF satisfiability problem belongs to NP complete class. It deals with Boolean expressions.

53. How many steps are required to prove that a decision problem is NP complete? a) 1 b) 2 c) 3 d) 4 Answer: b Explanation: First, the problem should be NP. Next, it should be proved that every problem in NP is reducible to the problem in question in polynomial time.

54. Which of the following problems is not NP complete? a) Hamiltonian circuit b) Bin packing c) Partition problem d) Halting problem Answer: d Explanation: Hamiltonian circuit, bin packing, partition problems are NP complete problems. Halting problem is an undecidable problem.

55. The choice of polynomial class has led to the development of an extensive theory called ________ a) computational complexity b) time complexity c) problem complexity d) decision complexity Answer: a Explanation: An extensive theory called computational complexity seeks to classify problems according to their inherent difficulty.

56. Master’s theorem is used for? a) solving recurrences b) solving iterative relations c) analysing loops d) calculating the time complexity of any code Answer: a Explanation: Master’s theorem is a direct method for solving recurrences. We can solve any recurrence that falls under any one of the three cases of master’s theorem.

57. How many cases are there under Master’s theorem? a) 2 b) 3 c) 4 d) 5 Answer: b Explanation: There are primarily 3 cases under master’s theorem. We can solve any recurrence that falls under any one of these three cases.

58. What is the result of the recurrences which fall under first case of Master’s theorem (let the recurrence be given by T(n)=aT(n/b)+f(n) and f(n)=nc? a) T(n) = O(n^logba) b) T(n) = O(nc log n) c) T(n) = O(f(n)) d) T(n) = O(n2) Answer: a Explanation: In first case of master’s theorem the necessary condition is that c < logba. If this condition is true then T(n) = O(n^logba).

59. What is the result of the recurrences which fall under second case of Master’s theorem (let the recurrence be given by T(n)=aT(n/b)+f(n) and f(n)=nc? a) T(n) = O(nlogba) b) T(n) = O(nc log n) c) T(n) = O(f(n)) d) T(n) = O(n2) Answer: b Explanation: In second case of master’s theorem the necessary condition is that c = logba. If this condition is true then T(n) = O(nc log n) Crack Job Placement Aptitude in First Attempt Prepare for Aptitude with 50+ Videos Lectures and Handmade Notes Click Here! 60. What is the result of the recurrences which fall under third case of Master’s theorem (let the recurrence be given by T(n)=aT(n/b)+f(n) and f(n)=nc? a) T(n) = O(nlogba) b) T(n) = O(nc log n) c) T(n) = O(f(n)) d) T(n) = O(n2) Answer: c Explanation: In third case of master’s theorem the necessary condition is that c > logba. If this condition is true then T(n) = O(f(n)).

61. We can solve any recurrence by using Master’s theorem. a) true b) false Answer: b Explanation: No we cannot solve all the recurrences by only using master’s theorem. We can solve only those which fall under the three cases prescribed in the theorem.

62. Under what case of Master’s theorem will the recurrence relation of merge sort fall? a) 1 b) 2 c) 3 d) It cannot be solved using master’s theorem Answer: b Explanation: The recurrence relation of merge sort is given by T(n) = 2T(n/2) + O(n). So we can observe that c = Logba so it will fall under case 2 of master’s theorem.

63. Under what case of Master’s theorem will the recurrence relation of stooge sort fall? a) 1 b) 2 c) 3 d) It cannot be solved using master’s theorem Answer: a Explanation: The recurrence relation of stooge sort is given as T(n) = 3T(2/3n) + O(1). It is found too be equal to O(n2.7) using master’s theorem first case.

64. Which case of master’s theorem can be extended further? a) 1 b) 2 c) 3 d) No case can be extended Answer: b Explanation: The second case of master’s theorem can be extended for a case where f(n) = nc (log n)k and the resulting recurrence becomes T(n)= O(nc (log n))k+1.

65. What is the result of the recurrences which fall under the extended second case of Master’s theorem (let the recurrence be given by T(n)=aT(n/b)+f(n) and f(n)=nc(log n)k? a) T(n) = O(nlogba) b) T(n) = O(nc log n) c) T(n)= O(nc (log n)k+1 d) T(n) = O(n2) Answer: c Explanation: In the extended second case of master’s theorem the necessary condition is that c = logba. If this condition is true then T(n)= O(nc(log n))k+1. Crack Job Placement Aptitude in First Attempt Prepare for Aptitude with 50+ Videos Lectures and Handmade Notes Click Here! 66. Under what case of Master’s theorem will the recurrence relation of binary search fall? a) 1 b) 2 c) 3 d) It cannot be solved using master’s theorem Answer: b Explanation: The recurrence relation of binary search is given by T(n) = T(n/2) + O(1). So we can observe that c = Logba so it will fall under case 2 of master’s theorem.

67. Consider the recurrence relation a1=4, an=5n+an-1. The value of a64 is _________ a) 10399 b) 23760 c) 75100 d) 53700 Answer: a Explanation: an=5n+an-1 = 5n + 5(n-1) + … + an-2 = 5n + 5(n-1) + 5(n − 2) +…+ a1 = 5n + 5(n-1) + 5(n − 2) +…+ 4 [since, a1=4] = 5n + 5(n-1) + 5(n − 2) +…+ 5.1 – 1 = 5(n + (n − 1)+…+2 + 1) – 1 = 5 * n(n+1)/ 2 – 1 an = 5 * n(n+1)/ 2 – 1 Now, n=64 so the answer is a64 = 10399.

68. Determine the solution of the recurrence relation Fn=20Fn-1 − 25Fn-2 where F0=4 and F1=14. a) an = 14*5n-1 b) an = 7/2*2n−1/2*6n c) an = 7/2*2n−3/4*6n+1 d) an = 3*2n−1/2*3n Answer: b Explanation: The characteristic equation of the recurrence relation is → x2−20x+36=0 So, (x-2)(x-18)=0. Hence, there are two real roots x1=2 and x2=18. Therefore the solution to the recurrence relation will have the form: an=a2n+b18n. To find a and b, set n=0 and n=1 to get a system of two equations with two unknowns: 4=a20+b180=a+b and 3=a21+b61=2a+6b. Solving this system gives b=-1/2 and a=7/2. So the solution to the recurrence relation is, an = 7/2*2n−1/2*6n.

69. What is the recurrence relation for 1, 7, 31, 127, 499? a) bn+1=5bn-1+3 b) bn=4bn+7! c) bn=4bn-1+3 d) bn=bn-1+1 Answer: c Explanation: Look at the differences between terms: 1, 7, 31, 124,…. and these are growing by a factor of 4. So, 1⋅4=4, 7⋅4=28, 31⋅4=124, and so on. Note that we always end up with 3 less than the next term. So, bn=4bn-1+3 is the recurrence relation and the initial condition is b0=1.

70. If Sn=4Sn-1+12n, where S0=6 and S1=7, find the solution for the recurrence relation. a) an=7(2n)−29/6n6n b) an=6(6n)+6/7n6n c) an=6(3n+1)−5n d) an=nn−2/6n6n Answer: b Explanation: The characteristic equation of the recurrence relation is → x2−4x-12=0 So, (x-6)(x+2)=0. Only the characteristic root is 6. Therefore the solution to the recurrence relation will have the form: an=a.6n+b.n.6n. To find a and b, set n=0 and n=1 to get a system of two equations with two unknowns: 6=a60+b.0.60=a and 7=a61+b.1.61=2a+6b. Solving this system gives a=6 and b=6/7. So the solution to the recurrence relation is, an=6(6n)−6/7n6n. Python Programming for Complete Beginners Start your Programming Journey with Python Programming which is Easy to Learn and Highly in Demand Click Here! 71. Find the value of a4 for the recurrence relation an=2an-1+3, with a0=6. a) 320 b) 221 c) 141 d) 65 Answer: c Explanation: When n=1, a1=2a0+3, Now a2=2a1+3. By substitution, we get a2=2(2a0+3)+3. Regrouping the terms, we get a4=141, where a0=6.

72. The solution to the recurrence relation an=an-1+2n, with initial term a0=2 are _________ a) 4n+7 b) 2(1+n) c) 3n2 d) 5*(n+1)/2 Answer: b Explanation: When n=1, a1=a0+2. By substitution we get, a2=a1+2 ⇒ a2=(a0+2)+2 and so on. So the solution to the recurrence relation, subject to the initial condition should be an=2+2n=2(1+n).

73. Determine the solution for the recurrence relation bn=8bn-1−12bn-2 with b0=3 and b1=4. a) 7/2*2n−1/2*6n b) 2/3*7n-5*4n c) 4!*6n d) 2/8n Answer: a Explanation: Rewrite the recurrence relation bn-8bn-1+12bn-2=0. Now from the characteristic equation: x2−8x+12=0 we have x: (x−2)(x−6)=0, so x=2 and x=6 are the characteristic roots. Therefore the solution to the recurrence relation will have the form: bn=b2n+c6n. To find b and c, set n=0 and n=1 to get a system of two equations with two unknowns: 3=b20+c60=b+c, and 4=b21+c61=2b+6c. Solving this system gives c=-1/2 and b=7/2. So the solution to the recurrence relation is, bn=7/2*2n−1/2*6n.

74. What is the solution to the recurrence relation an=5an-1+6an-2? a) 2n2 b) 6n c) (3/2)n d) n!*3 Answer: b Explanation: Check for the left side of the equation with all the options into the recurrence relation. Then, we get that 6n is the required solution to the recurrence relation an=5an-1 + 6an-2.

75. Determine the value of a2 for the recurrence relation an = 17an-1 + 30n with a0=3. a) 4387 b) 5484 c) 238 d) 1437 Answer: d Explanation: When n=1, a1=17a0+30, Now a2=17a1+30*2. By substitution, we get a2=17(17a0+30)+60. Then regrouping the terms, we get a2=1437, where a0=3.

1. The approach of dynamic programming is similar to a. Parsing b. Hash table c. Divide and Conquer algorithm d. Greedy algorithm Answer: C Divide and Conquer algorithm

2.The algorithms like merge sort, quick sort and binary search are based on a. Greedy algorithm b. Divide and Conquer algorithm c. Hash table d. Parsing Answer: D Divide and Conquer algorithm

3.The step(s) in the Divide and conquer process that takes a recursive approach is said to be a. Conquer/Solve b. Merge/Combine c. Divide/Break d. Both B and C Answer : C Divide/Break

4.The sub-problems in the dynamic programming are solved a. Dependently b. Independently c. Parallel d. Concurrent Answer: D Concurrent

5. In the Divide and Conquer process, breaking the problem into smaller sub-problems is the responsibility of a. Divide/Break b. Sorting/Divide c. Conquer/Solve d. Merge/Combine Answer: A Divide/Break Crack Job Placement Aptitude in First Attempt Prepare for Aptitude with 50+ Videos Lectures and Handmade Notes Click Here! 6.Which of the following algorithms is NOT a divide & conquer algorithm by nature? a.Euclidean algorithm to compute the greatest common divisor b.Heap Sort C.Cooley-Tukey fast Fourier transform d.Quick Sort Answer b Heap Sort

7.Consider the following C program int main() { int x, y, m, n; scanf (“%d %d”, &x, &y); /* x > 0 and y > 0 */ m = x; n = y; while (m != n) { if(m>n) m = m – n; else n = n – m; } printf(“%d”, n); }

What does the program compute? (GATE CS 2004) a.x + y using repeated subtraction b.x mod y using repeated subtraction c.the greatest common divisor of x and y d.the least common multiple of x and y Answer C the greatest common divisor of x and y

8.Consider the polynomial p(x) = a0 + a1x + a2x^2 +a3x^3, where ai != 0, for all i. The minimum number of multiplications needed to evaluate p on an input x is: a.3 b.4 c.6 d.9 Answer A 3

9.Maximum Subarray Sum problem is to find the subarray with maximum sum. For example, given an array {12, -13, -5, 25, -20, 30, 10}, the maximum subarray sum is 45. The naive solution for this problem is to calculate sum of all subarrays starting with every element and return the maximum of all. We can solve this using Divide and Conquer, what will be the worst case time complexity using Divide and Conquer. a.O(n) b.O(nLogn) c.O(Logn) d.O(n^2) Answer B O(nLogn)

10.Consider a situation where you don’t have function to calculate power (pow() function in C) and you need to calculate x^n where x can be any number and n is a positive integer. What can be the best possible time complexity of your power function? a.O(n) b.O(nLogn) c.O(LogLogn) d.O(Logn) Answer D O(Logn) Python Programming for Complete Beginners Start your Programming Journey with Python Programming which is Easy to Learn and Highly in Demand Click Here! 11.Consider the problem of searching an element x in an array ‘arr[]’ of size n. The problem can be solved in O(Logn) time if. 1) Array is sorted 2) Array is sorted and rotated by k. k is given to you and k <= n 3) Array is sorted and rotated by k. k is NOT given to you and k <= n 4) Array is not sorted a.1 Only b.1 & 2 only c.1, 2 and 3 only d.1, 2, 3 and 4 Answer C 1, 2 and 3 only

12.The secant method is used to find the root of an equation f(x) = 0. It is started from two distinct estimates xa and xb for the root. It is an iterative procedure involving linear interpolation to a root. The iteration stops if f(xb) is very small and then xb is the solution. The procedure is given below. Observe that there is an expression which is missing and is marked by? Which is the suitable expression that is to be put in place of? So that it follows all steps of the secant method? Secant Initialize: xa, xb, ε, N // ε = convergence indicator fb = f(xb) i = 0 while (i < N and |fb| > ε) do i = i + 1 // update counter xt = ? // missing expression for // intermediate value xa = xb // reset xa xb = xt // reset xb fb = f(xb) // function value at new xb end while if |fb| > ε then // loop is terminated with i = N write “Non-convergence” else write “return xb” end if a.xb – (fb– f(xa)) fb/ (xb – xa) b.xa– (fa– f(xa)) fa/ (xb – xa) c.xb – (fb – xa) fb/ (xb – fb(xa) d.xa – (xb – xa) fa/ (fb – f(xa)) Answer D xa – (xb – xa) fa/ (fb – f(xa))

1. Which of the following algorithms is the best approach for solving Huffman codes? a) exhaustive search b) greedy algorithm c) brute force algorithm d) divide and conquer algorithm Answer: b Explanation: Greedy algorithm is the best approach for solving the Huffman codes problem since it greedily searches for an optimal solution.

2. How many printable characters does the ASCII character set consists of? a) 120 b) 128 c) 100 d) 98 Answer: c Explanation: Out of 128 characters in an ASCII set, roughly, only 100 characters are printable while the rest are non-printable.

3. Which bit is reserved as a parity bit in an ASCII set? a) first b) seventh c) eighth d) tenth Answer: c Explanation: In an ASCII character set, seven bits are reserved for character representation while the eighth bit is a parity bit.

4. How many bits are needed for standard encoding if the size of the character set is X? a) log X b) X+1 c) 2X d) X2 Answer: a Explanation: If the size of the character set is X, then [log X] bits are needed for representation in a standard encoding.

5. The code length does not depend on the frequency of occurrence of characters. a) true b) false Answer: b Explanation: The code length depends on the frequency of occurrence of characters. The more frequent the character occurs, the less is the length of the code. Crack Job Placement Aptitude in First Attempt Prepare for Aptitude with 50+ Videos Lectures and Handmade Notes Click Here! 6. In Huffman coding, data in a tree always occur? a) roots b) leaves c) left sub trees d) right sub trees Answer: b Explanation: In Huffman encoding, data is always stored at the leaves of a tree inorder to compute the codeword effectively.

9. What will be the cost of the code if character ci is at depth di and occurs at frequency fi? a) cifi b) ∫cifi c) ∑fidi d) fidi Answer: c Explanation: If character ci is at depth di and occurs at frequency fi, the cost of the codeword obtained is ∑fidi.

10. An optimal code will always be present in a full tree. a) true b) false Answer: a Explanation: An optimal tree will always have the property that all nodes are either leaves or have two children. Otherwise, nodes with one child could move up a level. Crack Job Placement Aptitude in First Attempt Prepare for Aptitude with 50+ Videos Lectures and Handmade Notes Click Here! 11. The type of encoding where no character code is the prefix of another character code is called? a) optimal encoding b) prefix encoding c) frequency encoding d) trie encoding Answer: b Explanation: Even if the character codes are of different lengths, the encoding where no character code is the prefix of another character code is called prefix encoding.

12. What is the running time of the Huffman encoding algorithm? a) O(C) b) O(log C) c) O(C log C) d) O( N log C) Answer: c Explanation: If we maintain the trees in a priority queue, ordered by weight, then the running time is given by O(C log C).

13. What is the running time of the Huffman algorithm, if its implementation of the priority queue is done using linked lists? a) O(C) b) O(log C) c) O(C log C) d) O(C2) Answer: d Explanation: If the implementation of the priority queue is done using linked lists, the running time of Huffman algorithm is O(C2).

14. Kruskal’s algorithm is used to ______ a) find minimum spanning tree b) find single-source shortest path c) find all pair shortest path algorithm d) traverse the graph Answer: a Explanation: Kruskal’s algorithm is used to find the minimum spanning tree of the connected graph. It constructs the MST by finding the edge having the least possible weight that connects two trees in the forest.

17. What is the time complexity of Kruskal’s algorithm? a) O(log V) b) O(E log V) c) O(E2) d) O(V log E) Answer: b Explanation: Kruskal’s algorithm involves sorting of the edges, which takes O(E logE) time, where E is a number of edges in graph and V is the number of vertices. After sorting, all edges are iterated and union-find algorithm is applied. union-find algorithm requires O(logV) time. So, overall Kruskal’s algorithm requires O(E log V) time.

20. Which of the following is true? a) Prim’s algorithm can also be used for disconnected graphs b) Kruskal’s algorithm can also run on the disconnected graphs c) Prim’s algorithm is simpler than Kruskal’s algorithm d) In Kruskal’s sort edges are added to MST in decreasing order of their weights Answer: b Explanation: Prim’s algorithm iterates from one node to another, so it can not be applied for disconnected graph. Kruskal’s algorithm can be applied to the disconnected graphs to construct the minimum cost forest. Kruskal’s algorithm is comparatively easier and simpler than prim’s algorithm. Crack Job Placement Aptitude in First Attempt Prepare for Aptitude with 50+ Videos Lectures and Handmade Notes Click Here! 21. Which of the following is false about Kruskal’s algorithm? a) It is a greedy algorithm b) It constructs MST by selecting edges in increasing order of their weights c) It can accept cycles in the MST d) It uses a union-find data structure Answer: c Explanation: Kruskal’s algorithm is a greedy algorithm to construct the MST of the given graph. It constructs the MST by selecting edges in increasing order of their weights and rejects an edge if it may form the cycle. So, using Kruskal’s algorithm is never formed.

22. Kruskal’s algorithm is best suited for dense graphs than the prim’s algorithm. a) True b) False Answer: b Explanation: Prim’s algorithm outperforms the Kruskal’s algorithm in case of the dense graphs. It is significantly faster if graph has more edges than the Kruskal’s algorithm.

23. Consider the following statements. S1. Kruskal’s algorithm might produce a non-minimal spanning tree. S2. Kruskal’s algorithm can efficiently implemented using the disjoint-set data structure. a) S1 is true but S2 is false b) Both S1 and S2 are false c) Both S1 and S2 are true d) S2 is true but S1 is false Answer: d Explanation: In Kruskal’s algorithm, the disjoint-set data structure efficiently identifies the components containing a vertex and adds the new edges. And Kruskal’s algorithm always finds the MST for the connected graph.

24. Fractional knapsack problem is also known as __________ a) 0/1 knapsack problem b) Continuous knapsack problem c) Divisible knapsack problem d) Non continuous knapsack problem Answer: b Explanation: Fractional knapsack problem is also called continuous knapsack problem. Fractional knapsack is solved using dynamic programming.

25. Fractional knapsack problem is solved most efficiently by which of the following algorithm? a) Divide and conquer b) Dynamic programming c) Greedy algorithm d) Backtracking Answer: c Explanation: Greedy algorithm is used to solve this problem. We first sort items according to their value/weight ratio and then add item with highest ratio until we cannot add the next item as a whole. At the end, we add the next item as much as we can. Python Programming for Complete Beginners Start your Programming Journey with Python Programming which is Easy to Learn and Highly in Demand Click Here! 26. What is the objective of the knapsack problem? a) To get maximum total value in the knapsack b) To get minimum total value in the knapsack c) To get maximum weight in the knapsack d) To get minimum weight in the knapsack Answer: a Explanation: The objective is to fill the knapsack of some given volume with different materials such that the value of selected items is maximized.

27. Which of the following statement about 0/1 knapsack and fractional knapsack problem is correct? a) In 0/1 knapsack problem items are divisible and in fractional knapsack items are indivisible b) Both are the same c) 0/1 knapsack is solved using a greedy algorithm and fractional knapsack is solved using dynamic programming d) In 0/1 knapsack problem items are indivisible and in fractional knapsack items are divisible Answer: d Explanation: In fractional knapsack problem we can partially include an item into the knapsack whereas in 0/1 knapsack we have to either include or exclude the item wholly.

28. Time complexity of fractional knapsack problem is ____________ a) O(n log n) b) O(n) c) O(n2) d) O(nW) Answer: a Explanation: As the main time taking a step is of sorting so it defines the time complexity of our code. So the time complexity will be O(n log n) if we use quick sort for sorting.

29. Fractional knapsack problem can be solved in time O(n). a) True b) False Answer: a Explanation: It is possible to solve the problem in O(n) time by adapting the algorithm for finding weighted medians.

30. Given items as {value,weight} pairs {{40,20},{30,10},{20,5}}. The capacity of knapsack=20. Find the maximum value output assuming items to be divisible. a) 60 b) 80 c) 100 d) 40 Answer: a Explanation: The value/weight ratio are-{2,3,4}. So we include the second and third items wholly into the knapsack. This leaves only 5 units of volume for the first item. So we include the first item partially. Final value = 20+30+(40/4)=60. Crack Job Placement Aptitude in First Attempt Prepare for Aptitude with 50+ Videos Lectures and Handmade Notes Click Here! 31. The result of the fractional knapsack is greater than or equal to 0/1 knapsack. a) True b) False Answer: a Explanation: As fractional knapsack gives extra liberty to include the object partially which is not possible with 0/1 knapsack, thus we get better results with a fractional knapsack.

32. The main time taking step in fractional knapsack problem is ___________ a) Breaking items into a fraction b) Adding items into a knapsack c) Sorting d) Looping through sorted items Answer: c Explanation: The main time taking a step is to sort the items according to their value/weight ratio. It defines the time complexity of the code.

1. The Bellmann Ford algorithm returns _______ value. a) Boolean b) Integer c) String d) Double Answer: a Explanation: The Bellmann Ford algorithm returns Boolean value whether there is a negative weight cycle that is reachable from the source.

2. Bellmann ford algorithm provides solution for ____________ problems. a) All pair shortest path b) Sorting c) Network flow d) Single source shortest path Answer: d Explanation: Bellmann ford algorithm is used for finding solutions for single source shortest path problems. If the graph has no negative cycles that are reachable from the source then the algorithm produces the shortest paths and their weights.

3. Bellmann Ford algorithm is used to indicate whether the graph has negative weight cycles or not. a) True b) False Answer: a Explanation: Bellmann Ford algorithm returns true if the graph does not have any negative weight cycles and returns false when the graph has negative weight cycles.

4. How many solution/solutions are available for a graph having negative weight cycle? a) One solution b) Two solutions c) No solution d) Infinite solutions Answer: c Explanation: If the graph has any negative weight cycle then the algorithm indicates that no solution exists for that graph.

5. What is the running time of Bellmann Ford Algorithm? a) O(V) b) O(V2) c) O(ElogV) d) O(VE) Answer: d Explanation: Bellmann Ford algorithm runs in time O(VE), since the initialization takes O(V) for each of V-1 passes and the for loop in the algorithm takes O(E) time. Hence the total time taken by the algorithm is O(VE).

6. How many times the for loop in the Bellmann Ford Algorithm gets executed? a) V times b) V-1 c) E d) E-1 Answer: b Explanation: The for loop in the Bellmann Ford Algorithm gets executed for V-1 times. After making V-1 passes, the algorithm checks for a negative weight cycle and returns appropriate boolean value.

7. Dijikstra’s Algorithm is more efficient than Bellmann Ford Algorithm. a) True b) False Answer: a Explanation: The running time of Bellmann Ford Algorithm is O(VE) whereas Dijikstra’s Algorithm has running time of only O(V2).

8. Identify the correct Bellmann Ford Algorithm. a)

for i=1 to V[g]-1 do for each edge (u,v) in E[g] do Relax(u,v,w) for each edge (u,v) in E[g] do if d[v]>d[u]+w(u,v) then return False return True b)

for i=1 to V[g]-1 for each edge (u,v) in E[g] do if d[v]>d[u]+w(u,v) then return False return True c)

for i=1 to V[g]-1 do for each edge (u,v) in E[g] do Relax(u,v,w) for each edge (u,v) in E[g] do if d[v]<d[u]+w(u,v) then return true return True d)

for i=1 to V[g]-1 do for each edge (u,v) in E[g] do Relax(u,v,w) return True Answer: a Explanation: After initialization, the algorithm makes v-1 passes over the edges of the graph. Each pass is one iteration of the for loop and consists of relaxing each edge of the graph once. Then it checks for the negative weight cycle and returns an appropriate Boolean value.

9. What is the basic principle behind Bellmann Ford Algorithm? a) Interpolation b) Extrapolation c) Regression d) Relaxation Answer: d Explanation: Relaxation methods which are also called as iterative methods in which an approximation to the correct distance is replaced progressively by more accurate values till an optimum solution is found.

10. Bellmann Ford Algorithm can be applied for _____________ a) Undirected and weighted graphs b) Undirected and unweighted graphs c) Directed and weighted graphs d) All directed graphs Answer: c Explanation: Bellmann Ford Algorithm can be applied for all directed and weighted graphs. The weight function in the graph may either be positive or negative. Crack Job Placement Aptitude in First Attempt Prepare for Aptitude with 50+ Videos Lectures and Handmade Notes Click Here! 11. Bellmann Ford algorithm was first proposed by ________ a) Richard Bellmann b) Alfonso Shimbe c) Lester Ford Jr d) Edward F. Moore Answer: b Explanation: Alfonso Shimbe proposed Bellmann Ford algorithm in the year 1955. Later it was published by Richard Bellmann in 1957 and Lester Ford Jr in the year 1956. Hence it is called Bellmann Ford Algorithm.

14. Bellmann Ford Algorithm is an example for ____________ a) Dynamic Programming b) Greedy Algorithms c) Linear Programming d) Branch and Bound Answer: a Explanation: In Bellmann Ford Algorithm the shortest paths are calculated in bottom up manner which is similar to other dynamic programming problems.

15. A graph is said to have a negative weight cycle when? a) The graph has 1 negative weighted edge b) The graph has a cycle c) The total weight of the graph is negative d) The graph has 1 or more negative weighted edges Answer: c Explanation: When the total weight of the graph sums up to a negative number then the graph is said to have a negative weight cycle. Bellmann Ford Algorithm provides no solution for such graphs. Crack Job Placement Aptitude in First Attempt Prepare for Aptitude with 50+ Videos Lectures and Handmade Notes Click Here! 16. Which of the following methods can be used to solve the assembly line scheduling problem? a) Recursion b) Brute force c) Dynamic programming d) All of the mentioned Answer: d Explanation: All of the above-mentioned methods can be used to solve the assembly line scheduling problem.

17. What is the time complexity of the brute force algorithm used to solve the assembly line scheduling problem? a) O(1) b) O(n) c) O(n2) d) O(2n) Answer: d Explanation: In the brute force algorithm, all the possible ways are calculated which are of the order of 2n.

18. In the dynamic programming implementation of the assembly line scheduling problem, how many lookup tables are required? a) 0 b) 1 c) 2 d) 3 Answer: c Explanation: In the dynamic programming implementation of the assembly line scheduling problem, 2 lookup tables are required one for storing the minimum time and the other for storing the assembly line number.

19. Consider the following assembly line problem:

time_to_reach[2][3] = {{17, 2, 7}, {19, 4, 9}} time_spent[2][4] = {{6, 5, 15, 7}, {5, 10, 11, 4}} entry_time[2] = {8, 10} exit_time[2] = {10, 7} num_of_stations = 4 For the optimal solution which should be the starting assembly line? a) Line 1 b) Line 2 c) All of the mentioned d) None of the mentioned Answer: b Explanation: For the optimal solution, the starting assembly line is line 2.

20. Consider the following assembly line problem: time_to_reach[2][3] = {{17, 2, 7}, {19, 4, 9}} time_spent[2][4] = {{6, 5, 15, 7}, {5, 10, 11, 4}} entry_time[2] = {8, 10} exit_time[2] = {10, 7} num_of_stations = 4 For the optimal solution, which should be the exit assembly line? a) Line 1 b) Line 2 c) All of the mentioned d) None of the mentioned Answer: b Explanation: For the optimal solution, the exit assembly line is line 2. Learn Machine Learning with Python from Scratch Start your Machine learning & Data Science journey with Complete Hands-on Learning & doubt solving Support Click Here! 21. Consider the following assembly line problem: time_to_reach[2][3] = {{17, 2, 7}, {19, 4, 9}} time_spent[2][4] = {{6, 5, 15, 7}, {5, 10, 11, 4}} entry_time[2] = {8, 10} exit_time[2] = {10, 7} num_of_stations = 4 What is the minimum time required to build the car chassis? a) 40 b) 41 c) 42 d) 43 Answer: d Explanation: The minimum time required is 43. The path is S2,1 -> S1,2 -> S2,3 -> S2,4, where Si,j : i = line number, j = station number

22. Consider the following code:

#include<stdio.h> int get_min(int a, int b) { if(a<b) return a; return b; } int minimum_time_required(int reach[][3],int spent[][4], int *entry, int *exit, int n) { int t1[n], t2[n],i; t1[0] = entry[0] + spent[0][0]; t2[0] = entry[1] + spent[1][0]; for(i = 1; i < n; i++) { t1[i] = get_min(t1[i-1]+spent[0][i], t2[i-1]+reach[1][i-1]+spent[0][i]); __________; } return get_min(t1[n-1]+exit[0], t2[n-1]+exit[1]); } Which of the following lines should be inserted to complete the above code? a) t2[i] = get_min(t2[i-1]+spent[1][i], t1[i-1]+reach[0][i-1]+spent[1][i]) b) t2[i] = get_min(t2[i-1]+spent[1][i], t1[i-1]+spent[1][i]) c) t2[i] = get_min(t2[i-1]+spent[1][i], t1[i-1]+reach[0][i-1]) d) none of the mentioned Answer: a Explanation: The line t2[i] = get_min(t2[i-1]+spent[1][i], t1[i-1]+reach[0][i-1]+spent[1][i]) should be added to complete the above code.

23. What is the time complexity of the above dynamic programming implementation of the assembly line scheduling problem? a) O(1) b) O(n) c) O(n2) d) O(n3) Answer: b Explanation: The time complexity of the above dynamic programming implementation of the assembly line scheduling problem is O(n).

24. What is the space complexity of the above dynamic programming implementation of the assembly line scheduling problem? a) O(1) b) O(n) c) O(n2) d) O(n3) Answer: b Explanation: The space complexity of the above dynamic programming implementation of the assembly line scheduling problem is O(n).

25. What is the output of the following code?

#include<stdio.h> int get_min(int a, int b) { if(a<b) return a; return b; } int minimum_time_required(int reach[][3],int spent[][4], int *entry, int *exit, int n) { int t1[n], t2[n], i; t1[0] = entry[0] + spent[0][0]; t2[0] = entry[1] + spent[1][0]; for(i = 1; i < n; i++) { t1[i] = get_min(t1[i-1]+spent[0][i], t2[i-1]+reach[1][i-1]+spent[0][i]); t2[i] = get_min(t2[i-1]+spent[1][i], t1[i-1]+reach[0][i-1]+spent[1][i]); } return get_min(t1[n-1]+exit[0], t2[n-1]+exit[1]); } int main() { int time_to_reach[][3] = {{6, 1, 5}, {2, 4, 7}}; int time_spent[][4] = {{6, 5, 4, 7}, {5, 10, 2, 6}}; int entry_time[2] = {5, 6}; int exit_time[2] = {8, 9}; int num_of_stations = 4; int ans = minimum_time_required(time_to_reach, time_spent, entry_time, exit_time, num_of_stations); printf(“%d”,ans); return 0; } a) 32 b) 33 c) 34 d) 35 Answer: c Explanation: The program prints the optimal time required to build the car chassis, which is 34. Crack Job Placement Aptitude in First Attempt Prepare for Aptitude with 50+ Videos Lectures and Handmade Notes Click Here! 26. What is the value stored in t1[2] when the following code is executed? #include<stdio.h> int get_min(int a, int b) { if(a<b) return a; return b; } int minimum_time_required(int reach[][3],int spent[][4], int *entry, int *exit, int n) { int t1[n], t2[n],i; t1[0] = entry[0] + spent[0][0]; t2[0] = entry[1] + spent[1][0]; for(i = 1; i < n; i++) { t1[i] = get_min(t1[i-1]+spent[0][i], t2[i-1]+reach[1][i-1]+spent[0][i]); t2[i] = get_min(t2[i-1]+spent[1][i], t1[i-1]+reach[0][i-1]+spent[1][i]); } return get_min(t1[n-1]+exit[0], t2[n-1]+exit[1]); } int main() { int time_to_reach[][3] = {{6, 1, 5}, {2, 4, 7}}; int time_spent[][4] = {{6, 5, 4, 7}, {5, 10, 2, 6}}; int entry_time[2] = {5, 6}; int exit_time[2] = {8, 9}; int num_of_stations = 4; int ans = minimum_time_required(time_to_reach, time_spent, entry_time, exit_time, num_of_stations); printf(“%d”,ans); return 0; } a) 16 b) 18 c) 20 d) 22 Answer: c Explanation: The value stored in t1[2] when the above code is executed is 20.

27. What is the value stored in t2[3] when the following code is executed? #include<stdio.h> int get_min(int a, int b) { if(a<b) return a; return b; } int minimum_time_required(int reach[][3],int spent[][4], int *entry, int *exit, int n) { int t1[n], t2[n],i; t1[0] = entry[0] + spent[0][0]; t2[0] = entry[1] + spent[1][0]; for(i = 1; i < n; i++) { t1[i] = get_min(t1[i-1]+spent[0][i], t2[i-1]+reach[1][i-1]+spent[0][i]); t2[i] = get_min(t2[i-1]+spent[1][i], t1[i-1]+reach[0][i-1]+spent[1][i]); } return get_min(t1[n-1]+exit[0], t2[n-1]+exit[1]); } int main() { int time_to_reach[][3] = {{6, 1, 5}, {2, 4, 7}}; int time_spent[][4] = {{6, 5, 4, 7}, {5, 10, 2, 6}}; int entry_time[2] = {5, 6}; int exit_time[2] = {8, 9}; int num_of_stations = 4; int ans = minimum_time_required(time_to_reach, time_spent, entry_time, exit_time, num_of_stations); printf(“%d”,ans); return 0; } a) 19 b) 23 c) 25 d) 27 Answer: c Explanation: The value stored in t2[3] when the above code is executed is 25.

28. What is the output of the following code? #include<stdio.h> int get_min(int a, int b) { if(a<b) return a; return b; } int minimum_time_required(int reach[][4],int spent[][5], int *entry, int *exit, int n) { int t1[n], t2[n], i; t1[0] = entry[0] + spent[0][0]; t2[0] = entry[1] + spent[1][0]; for(i = 1; i < n; i++) { t1[i] = get_min(t1[i-1]+spent[0][i], t2[i-1]+reach[1][i-1]+spent[0][i]); t2[i] = get_min(t2[i-1]+spent[1][i], t1[i-1]+reach[0][i-1]+spent[1][i]); } return get_min(t1[n-1]+exit[0], t2[n-1]+exit[1]); } int main() { int time_to_reach[][4] = {{16, 10, 5, 12}, {12, 4, 17, 8}}; int time_spent[][5] = {{13, 5, 20, 19, 9}, {15, 10, 12, 16, 13}}; int entry_time[2] = {12, 9}; int exit_time[2] = {10, 13}; int num_of_stations = 5; int ans = minimum_time_required(time_to_reach, time_spent, entry_time, exit_time, num_of_stations); printf(“%d”,ans); return 0; } a) 62 b) 69 c) 75 d) 88 Answer: d Explanation: The program prints the optimal time required to build the car chassis, which is 88.

29. Floyd Warshall’s Algorithm is used for solving ____________ a) All pair shortest path problems b) Single Source shortest path problems c) Network flow problems d) Sorting problems Answer: a Explanation: Floyd Warshall’s Algorithm is used for solving all pair shortest path problems. It means the algorithm is used for finding the shortest paths between all pairs of vertices in a graph.

30. Floyd Warshall’s Algorithm can be applied on __________ a) Undirected and unweighted graphs b) Undirected graphs c) Directed graphs d) Acyclic graphs Answer: c Explanation: Floyd Warshall Algorithm can be applied in directed graphs. From a given directed graph, an adjacency matrix is framed and then all pair shortest path is computed by the Floyd Warshall Algorithm. Python Programming for Complete Beginners Start your Programming Journey with Python Programming which is Easy to Learn and Highly in Demand Click Here! 31. What is the running time of the Floyd Warshall Algorithm? a) Big-oh(V) b) Theta(V2) c) Big-Oh(VE) d) Theta(V3) Answer: d Explanation: The running time of the Floyd Warshall algorithm is determined by the triply nested for loops. Since each execution of the for loop takes O(1) time, the algorithm runs in time Theta(V3).

32. What approach is being followed in Floyd Warshall Algorithm? a) Greedy technique b) Dynamic Programming c) Linear Programming d) Backtracking Answer: b Explanation: Floyd Warshall Algorithm follows dynamic programming approach because the all pair shortest paths are computed in bottom up manner.

33. Floyd Warshall Algorithm can be used for finding _____________ a) Single source shortest path b) Topological sort c) Minimum spanning tree d) Transitive closure Answer: d Explanation: One of the ways to compute the transitive closure of a graph in Theta(N3) time is to assign a weight of 1 to each edge of E and then run the Floyd Warshall Algorithm.

34. What procedure is being followed in Floyd Warshall Algorithm? a) Top down b) Bottom up c) Big bang d) Sandwich Answer: b Explanation: Bottom up procedure is being used to compute the values of the matrix elements dij(k). The input is an n x n matrix. The procedure returns the matrix D(n) of the shortest path weights.

35. Floyd- Warshall algorithm was proposed by ____________ a) Robert Floyd and Stephen Warshall b) Stephen Floyd and Robert Warshall c) Bernad Floyd and Robert Warshall d) Robert Floyd and Bernad Warshall Answer: a Explanation: Floyd- Warshall Algorithm was proposed by Robert Floyd in the year 1962. The same algorithm was proposed by Stephen Warshall during the same year for finding the transitive closure of the graph. Crack Job Placement Aptitude in First Attempt Prepare for Aptitude with 50+ Videos Lectures and Handmade Notes Click Here! 36. Who proposed the modern formulation of Floyd-Warshall Algorithm as three nested loops? a) Robert Floyd b) Stephen Warshall c) Bernard Roy d) Peter Ingerman Answer: d Explanation: The modern formulation of Floyd-Warshall Algorithm as three nested for-loops was proposed by Peter Ingerman in the year 1962.

37. Complete the program. n=rows[W] D(0)=W for k=1 to n do for i=1 to n do for j=1 to n do ________________________________ return D(n) a) dij(k)=min(dij(k-1), dik(k-1) – dkj(k-1)) b) dij(k)=max(dij(k-1), dik(k-1) – dkj(k-1)) c) dij(k)=min(dij(k-1), dik(k-1) + dkj(k-1)) d) dij(k)=max(dij(k-1), dik(k-1) + dkj(k-1)) Answer: c Explanation: In order to compute the shortest path from vertex i to vertex j, we need to find the minimum of 2 values which are dij(k-1) and sum of dik(k-1) and dkj(k-1).

38. What happens when the value of k is 0 in the Floyd Warshall Algorithm? a) 1 intermediate vertex b) 0 intermediate vertex c) N intermediate vertices d) N-1 intermediate vertices Answer: b Explanation: When k=0, a path from vertex i to vertex j has no intermediate vertices at all. Such a path has at most one edge and hence dij(0) = wij.

39. Using logical operator’s instead arithmetic operators saves time and space. a) True b) False Answer: a Explanation: In computers, logical operations on single bit values execute faster than arithmetic operations on integer words of data.

43. What is the formula to compute the transitive closure of a graph? a) tij(k) = tij(k-1) AND (tik(k-1) OR tkj(k-1)) b) tij(k) = tij(k-1) OR (tik(k-1) AND tkj(k-1)) c) tij(k) = tij(k-1) AND (tik(k-1) AND tkj(k-1)) d) tij(k) = tij(k-1) OR (tik(k-1) OR tkj(k-1)) Answer: b Explanation: Transitive closure of a graph can be computed by using Floyd Warshall algorithm. This method involves substitution of logical operations (logical OR and logical AND) for arithmetic operations min and + in Floyd Warshall Algorithm. Floyd Warshall Algorithm: dij(k)=min(dij(k-1), dik(k-1) + dkj(k-1)) Transitive closure: tij(k)= tij(k-1) OR (tik(k-1) AND tkj(k-1)).

42. Which of the following methods can be used to solve the longest common subsequence problem? a) Recursion b) Dynamic programming c) Both recursion and dynamic programming d) Greedy algorithm Answer: c Explanation: Both recursion and dynamic programming can be used to solve the longest subsequence problem.

43. Consider the strings “PQRSTPQRS” and “PRATPBRQRPS”. What is the length of the longest common subsequence? a) 9 b) 8 c) 7 d) 6 Answer: c Explanation: The longest common subsequence is “PRTPQRS” and its length is 7.

44. Which of the following problems can be solved using the longest subsequence problem? a) Longest increasing subsequence b) Longest palindromic subsequence c) Longest bitonic subsequence d) Longest decreasing subsequence Answer: b Explanation: To find the longest palindromic subsequence in a given string, reverse the given string and then find the longest common subsequence in the given string and the reversed string.

45. Longest common subsequence is an example of ____________ a) Greedy algorithm b) 2D dynamic programming c) 1D dynamic programming d) Divide and conquer Answer: b Explanation: Longest common subsequence is an example of 2D dynamic programming. Learn Machine Learning with Python from Scratch Start your Machine learning & Data Science journey with Complete Hands-on Learning & doubt solving Support Click Here! 46. What is the time complexity of the brute force algorithm used to find the longest common subsequence? a) O(n) b) O(n2) c) O(n3) d) O(2n) Answer: d Explanation: The time complexity of the brute force algorithm used to find the longest common subsequence is O(2n).

47. Consider the following dynamic programming implementation of the longest common subsequence problem: #include<stdio.h> #include<string.h> int max_num(int a, int b) { if(a > b) return a; return b; } int lcs(char *str1, char *str2) { int i,j,len1,len2; len1 = strlen(str1); len2 = strlen(str2); int arr[len1 + 1][len2 + 1]; for(i = 0; i <= len1; i++) arr[i][0] = 0; for(i = 0; i <= len2; i++) arr[0][i] = 0; for(i = 1; i <= len1; i++) { for(j = 1; j <= len2; j++) { if(str1[i-1] == str2[j – 1]) ______________; else arr[i][j] = max_num(arr[i – 1][j], arr[i][j – 1]); } } return arr[len1][len2]; } int main() { char str1[] = ” abcedfg”, str2[] = “bcdfh”; int ans = lcs(str1,str2); printf(“%d”,ans); return 0; } Which of the following lines completes the above code? a) arr[i][j] = 1 + arr[i][j]. b) arr[i][j] = 1 + arr[i – 1][j – 1]. c) arr[i][j] = arr[i – 1][j – 1]. d) arr[i][j] = arr[i][j]. Answer: b Explanation: The line, arr[i][j] = 1 + arr[i – 1][j – 1] completes the above code.

48. What is the time complexity of the following dynamic programming implementation of the longest common subsequence problem where length of one string is “m” and the length of the other string is “n”? #include<stdio.h> #include<string.h> int max_num(int a, int b) { if(a > b) return a; return b; } int lcs(char *str1, char *str2) { int i,j,len1,len2; len1 = strlen(str1); len2 = strlen(str2); int arr[len1 + 1][len2 + 1]; for(i = 0; i <= len1; i++) arr[i][0] = 0; for(i = 0; i <= len2; i++) arr[0][i] = 0; for(i = 1; i <= len1; i++) { for(j = 1; j <= len2; j++) { if(str1[i-1] == str2[j – 1]) arr[i][j] = 1 + arr[i – 1][j – 1]; else arr[i][j] = max_num(arr[i – 1][j], arr[i][j – 1]); } } return arr[len1][len2]; } int main() { char str1[] = ” abcedfg”, str2[] = “bcdfh”; int ans = lcs(str1,str2); printf(“%d”,ans); return 0; } a) O(n) b) O(m) c) O(m + n) d) O(mn) Answer: d Explanation: The time complexity of the above dynamic programming implementation of the longest common subsequence is O(mn).

49. What is the space complexity of the following dynamic programming implementation of the longest common subsequence problem where length of one string is “m” and the length of the other string is “n”? #include<stdio.h> #include<string.h> int max_num(int a, int b) { if(a > b) return a; return b; } int lcs(char *str1, char *str2) { int i,j,len1,len2; len1 = strlen(str1); len2 = strlen(str2); int arr[len1 + 1][len2 + 1]; for(i = 0; i <= len1; i++) arr[i][0] = 0; for(i = 0; i <= len2; i++) arr[0][i] = 0; for(i = 1; i <= len1; i++) { for(j = 1; j <= len2; j++) { if(str1[i-1] == str2[j – 1]) arr[i][j] = 1 + arr[i – 1][j – 1]; else arr[i][j] = max_num(arr[i – 1][j], arr[i][j – 1]); } } return arr[len1][len2]; } int main() { char str1[] = ” abcedfg”, str2[] = “bcdfh”; int ans = lcs(str1,str2); printf(“%d”,ans); return 0; } a) O(n) b) O(m) c) O(m + n) d) O(mn) Answer: d Explanation: The space complexity of the above dynamic programming implementation of the longest common subsequence is O(mn).

50. What is the output of the following code? #include<stdio.h> #include<string.h> int max_num(int a, int b) { if(a > b) return a; return b; } int lcs(char *str1, char *str2) { int i,j,len1,len2; len1 = strlen(str1); len2 = strlen(str2); int arr[len1 + 1][len2 + 1]; for(i = 0; i <= len1; i++) arr[i][0] = 0; for(i = 0; i <= len2; i++) arr[0][i] = 0; for(i = 1; i <= len1; i++) { for(j = 1; j <= len2; j++) { if(str1[i-1] == str2[j – 1]) arr[i][j] = 1 + arr[i – 1][j – 1]; else arr[i][j] = max_num(arr[i – 1][j], arr[i][j – 1]); } } return arr[len1][len2]; } int main() { char str1[] = “hbcfgmnapq”, str2[] = “cbhgrsfnmq”; int ans = lcs(str1,str2); printf(“%d”,ans); return 0; } a) 3 b) 4 c) 5 d) 6 Answer: b Explanation: The program prints the length of the longest common subsequence, which is 4. Crack Job Placement Aptitude in First Attempt Prepare for Aptitude with 50+ Videos Lectures and Handmade Notes Click Here! 51. Which of the following is the longest common subsequence between the strings “hbcfgmnapq” and “cbhgrsfnmq” ? a) hgmq b) cfnq c) bfmq d) fgmna Answer: d Explanation: The length of the longest common subsequence is 4. But ‘fgmna’ is not the longest common subsequence as its length is 5.

52. What is the value stored in arr[2][3] when the following code is executed? #include<stdio.h> #include<string.h> int max_num(int a, int b) { if(a > b) return a; return b; } int lcs(char *str1, char *str2) { int i,j,len1,len2; len1 = strlen(str1); len2 = strlen(str2); int arr[len1 + 1][len2 + 1]; for(i = 0; i <= len1; i++) arr[i][0] = 0; for(i = 0; i <= len2; i++) arr[0][i] = 0; for(i = 1; i <= len1; i++) { for(j = 1; j <= len2; j++) { if(str1[i-1] == str2[j – 1]) arr[i][j] = 1 + arr[i – 1][j – 1]; else arr[i][j] = max_num(arr[i – 1][j], arr[i][j – 1]); } } return arr[len1][len2]; } int main() { char str1[] = “hbcfgmnapq”, str2[] = “cbhgrsfnmq”; int ans = lcs(str1,str2); printf(“%d”,ans); return 0; } a) 1 b) 2 c) 3 d) 4 Answer: a Explanation: The value stored in arr[2][3] is 1.

53. What is the output of the following code? #include<stdio.h> #include<string.h> int max_num(int a, int b) { if(a > b) return a; return b; } int lcs(char *str1, char *str2) { int i,j,len1,len2; len1 = strlen(str1); len2 = strlen(str2); int arr[len1 + 1][len2 + 1]; for(i = 0; i <= len1; i++) arr[i][0] = 0; for(i = 0; i <= len2; i++) arr[0][i] = 0; for(i = 1; i <= len1; i++) { for(j = 1; j <= len2; j++) { if(str1[i-1] == str2[j – 1]) arr[i][j] = 1 + arr[i – 1][j – 1]; else arr[i][j] = max_num(arr[i – 1][j], arr[i][j – 1]); } } return arr[len1][len2]; } int main() { char str1[] = “abcd”, str2[] = “efgh”; int ans = lcs(str1,str2); printf(“%d”,ans); return 0; } a) 3 b) 2 c) 1 d) 0 Answer: d Explanation: The program prints the length of the longest common subsequence, which is 0.

54. Which of the following methods can be used to solve the longest palindromic subsequence problem? a) Dynamic programming b) Recursion c) Brute force d) Dynamic programming, Recursion, Brute force Answer: d Explanation: Dynamic programming, Recursion, Brute force can be used to solve the longest palindromic subsequence problem.

55. Which of the following is not a palindromic subsequence of the string “ababcdabba”? a) abcba b) abba c) abbbba d) adba Answer: d Explanation: ‘adba’ is not a palindromic sequence.

55. For which of the following, the length of the string is not equal to the length of the longest palindromic subsequence? a) A string that is a palindrome b) A string of length one c) A string that has all the same letters(e.g. aaaaaa) d) Some strings of length two Answer: d Explanation: A string of length 2 for eg: ab is not a palindrome. Python Programming for Complete Beginners Start your Programming Journey with Python Programming which is Easy to Learn and Highly in Demand Click Here! 56. What is the length of the longest palindromic subsequence for the string “ababcdabba”? a) 6 b) 7 c) 8 d) 9 Answer: b Explanation: The longest palindromic subsequence is “abbabba” and its length is 7.

57. What is the time complexity of the brute force algorithm used to find the length of the longest palindromic subsequence? a) O(1) b) O(2n) c) O(n) d) O(n2) Answer: b Explanation: In the brute force algorithm, all the subsequences are found and the length of the longest palindromic subsequence is calculated. This takes exponential time.

58. For every non-empty string, the length of the longest palindromic subsequence is at least one. a) True b) False Answer: a Explanation: A single character of any string can always be considered as a palindrome and its length is one.

59. Longest palindromic subsequence is an example of ______________ a) Greedy algorithm b) 2D dynamic programming c) 1D dynamic programming d) Divide and conquer Answer: b Explanation: Longest palindromic subsequence is an example of 2D dynamic programming.

60. Consider the following code: #include<stdio.h> #include<string.h> int max_num(int a, int b) { if(a > b) return a; return b; } int lps(char *str1) { int i,j,len; len = strlen(str1); char str2[len + 1]; strcpy(str2, str1); ______________; int arr[len + 1][len + 1]; for(i = 0; i <= len; i++) arr[i][0] = 0; for(i = 0; i <= len; i++) arr[0][i] = 0; for(i = 1; i <= len; i++) { for(j = 1; j <= len; j++) { if(str1[i-1] == str2[j – 1]) arr[i][j] = 1 + arr[i – 1][j – 1]; else arr[i][j] = max_num(arr[i – 1][j], arr[i][j – 1]); } } return arr[len][len]; } int main() { char str1[] = “ababcdabba”; int ans = lps(str1); printf(“%d”,ans); return 0; } Which of the following lines completes the above code? a) strrev(str2) b) str2 = str1 c) len2 = strlen(str2) d) strlen(str2) Answer: a Explanation: To find the longest palindromic subsequence, we need to reverse the copy of the string, which is done by strrev. Crack Job Placement Aptitude in First Attempt Prepare for Aptitude with 50+ Videos Lectures and Handmade Notes Click Here! 61. What is the time complexity of the following dynamic programming implementation to find the longest palindromic subsequence where the length of the string is n? #include<stdio.h> #include<string.h> int max_num(int a, int b) { if(a > b) return a; return b; } int lps(char *str1) { int i,j,len; len = strlen(str1); char str2[len + 1]; strcpy(str2, str1); strrev(str2); int arr[len + 1][len + 1]; for(i = 0; i <= len; i++) arr[i][0] = 0; for(i = 0; i <= len; i++) arr[0][i] = 0; for(i = 1; i <= len; i++) { for(j = 1; j <= len; j++) { if(str1[i-1] == str2[j – 1]) arr[i][j] = 1 + arr[i – 1][j – 1]; else arr[i][j] = max_num(arr[i – 1][j], arr[i][j – 1]); } } return arr[len][len]; } int main() { char str1[] = “ababcdabba”; int ans = lps(str1); printf(“%d”,ans); return 0; } a) O(n) b) O(1) c) O(n2) d) O(2) Answer: c Explanation: The time complexity of the above dynamic programming implementation to find the longest palindromic subsequence is O(n2).

62. What is the space complexity of the following dynamic programming implementation to find the longest palindromic subsequence where the length of the string is n? #include<stdio.h> #include<string.h> int max_num(int a, int b) { if(a > b) return a; return b; } int lps(char *str1) { int i,j,len; len = strlen(str1); char str2[len + 1]; strcpy(str2, str1); strrev(str2); int arr[len + 1][len + 1]; for(i = 0; i <= len; i++) arr[i][0] = 0; for(i = 0; i <= len; i++) arr[0][i] = 0; for(i = 1; i <= len; i++) { for(j = 1; j <= len; j++) { if(str1[i-1] == str2[j – 1]) arr[i][j] = 1 + arr[i – 1][j – 1]; else arr[i][j] = max_num(arr[i – 1][j], arr[i][j – 1]); } } return arr[len][len]; } int main() { char str1[] = “ababcdabba”; int ans = lps(str1); printf(“%d”,ans); return 0; } a) O(n) b) O(1) c) O(n2) d) O(2) Answer: c Explanation: The space complexity of the above dynamic programming implementation to find the longest palindromic subsequence is O(n2).

63. What is the value stored in arr[3][3] when the following code is executed? #include<stdio.h> #include<string.h> int max_num(int a, int b) { if(a > b) return a; return b; } int lps(char *str1) { int i,j,len; len = strlen(str1); char str2[len + 1]; strcpy(str2, str1); strrev(str2); int arr[len + 1][len + 1]; for(i = 0; i <= len; i++) arr[i][0] = 0; for(i = 0; i <= len; i++) arr[0][i] = 0; for(i = 1; i <= len; i++) { for(j = 1; j <= len; j++) { if(str1[i-1] == str2[j – 1]) arr[i][j] = 1 + arr[i – 1][j – 1]; else arr[i][j] = max_num(arr[i – 1][j], arr[i][j – 1]); } } return arr[len][len]; } int main() { char str1[] = “ababcdabba”; int ans = lps(str1); printf(“%d”,ans); return 0; } a) 2 b) 3 c) 4 d) 5 Answer: a Explanation: The value stored in arr[3][3] when the above code is executed is 2.

64. What is the output of the following code? #include<stdio.h> #include<string.h> int max_num(int a, int b) { if(a > b) return a; return b; } int lps(char *str1) { int i,j,len; len = strlen(str1); char str2[len + 1]; strcpy(str2, str1); strrev(str2); int arr[len + 1][len + 1]; for(i = 0; i <= len; i++) arr[i][0] = 0; for(i = 0; i <= len; i++) arr[0][i] = 0; for(i = 1; i <= len; i++) { for(j = 1; j <= len; j++) { if(str1[i-1] == str2[j – 1]) arr[i][j] = 1 + arr[i – 1][j – 1]; else arr[i][j] = max_num(arr[i – 1][j], arr[i][j – 1]); } } return arr[len][len]; } int main() { char str1[] = “abcd”; int ans = lps(str1); printf(“%d”,ans); return 0; } a) 0 b) 1 c) 2 d) 3 Answer: b Explanation: The program prints the length of the longest palindromic subsequence, which is 1.

1. Which of the problems cannot be solved by backtracking method? a) n-queen problem b) subset sum problem c) hamiltonian circuit problem d) travelling salesman problem Answer: d Explanation: N-queen problem, subset sum problem, Hamiltonian circuit problems can be solved by backtracking method whereas travelling salesman problem is solved by Branch and bound method.

2. Backtracking algorithm is implemented by constructing a tree of choices called as? a) State-space tree b) State-chart tree c) Node tree d) Backtracking tree Answer: a Explanation: Backtracking problem is solved by constructing a tree of choices called as the state-space tree. Its root represents an initial state before the search for a solution begins.

3. What happens when the backtracking algorithm reaches a complete solution? a) It backtracks to the root b) It continues searching for other possible solutions c) It traverses from a different route d) Recursively traverses through the same route Answer: b Explanation: When we reach a final solution using a backtracking algorithm, we either stop or continue searching for other possible solutions.

4. A node is said to be ____________ if it has a possibility of reaching a complete solution. a) Non-promising b) Promising c) Succeeding d) Preceding Answer: b Explanation: If a node has a possibility of reaching the final solution, it is called a promising node. Otherwise, it is non-promising.

5. In what manner is a state-space tree for a backtracking algorithm constructed? a) Depth-first search b) Breadth-first search c) Twice around the tree d) Nearest neighbour first Answer: a Explanation: A state-space tree for a backtracking algorithm is constructed in the manner of depth-first search so that it is easy to look into.

6. The leaves in a state-space tree represent only complete solutions. a) true b) false Answer: b Explanation: The leaves in a state space tree can either represent non-promising dead ends or complete solutions found by the algorithm.

7. In general, backtracking can be used to solve? a) Numerical problems b) Exhaustive search c) Combinatorial problems d) Graph coloring problems Answer: c Explanation: Backtracking approach is used to solve complex combinatorial problems which cannot be solved by exhaustive search algorithms.

8. Which one of the following is an application of the backtracking algorithm? a) Finding the shortest path b) Finding the efficient quantity to shop c) Ludo d) Crossword Answer: d Explanation: Crossword puzzles are based on backtracking approach whereas the rest are travelling salesman problem, knapsack problem and dice game.

9. Backtracking algorithm is faster than the brute force technique a) true b) false Answer: a Explanation: Backtracking is faster than brute force approach since it can remove a large set of answers in one test.

10. Which of the following logical programming languages is not based on backtracking? a) Icon b) Prolog c) Planner d) Fortran Answer: d Explanation: Backtracking algorithm form the basis for icon, planner and prolog whereas fortran is an ancient assembly language used in second generation computers. Crack Job Placement Aptitude in First Attempt Prepare for Aptitude with 50+ Videos Lectures and Handmade Notes Click Here! 11. The problem of finding a list of integers in a given specific range that meets certain conditions is called? a) Subset sum problem b) Constraint satisfaction problem c) Hamiltonian circuit problem d) Travelling salesman problem Answer: b Explanation: Constraint satisfaction problem is the problem of finding a list of integers under given constraints. Constraint satisfaction problem is solved using a backtracking approach.

12. Who coined the term ‘backtracking’? a) Lehmer b) Donald c) Ross d) Ford Answer: a Explanation: D.H. Lehmer was the first person to coin the term backtracking. Initially, the backtracking facility was provided using SNOBOL.

13. ___________ enumerates a list of promising nodes that could be computed to give the possible solutions of a given problem. a) Exhaustive search b) Brute force c) Backtracking d) Divide and conquer Answer: c Explanation: Backtracking is a general algorithm that evaluates partially constructed candidates that can be developed further without violating problem constraints.

14. The problem of finding a subset of positive integers whose sum is equal to a given positive integer is called as? a) n- queen problem b) subset sum problem c) knapsack problem d) hamiltonian circuit problem Answer: b Explanation: Subset sum problem is the problem of finding a subset using the backtracking algorithm when summed, equals a given integer.

15. Who published the eight queens puzzle? a) Max Bezzel b) Carl c) Gauss d) Friedrich Answer: a Explanation: The first Eight Queen Puzzle was published by Max Friedrich William Bezzel, who was a chess composer by profession in 1848. He was a German chess composer and the first person to publish the puzzle.

16. When was the Eight Queen Puzzle published? a) 1846 b) 1847 c) 1848 d) 1849 Answer: c Explanation: The first Eight Queen Puzzle was published by Max Friedrich William Bezzel, who was a German chess composer by profession. He published the puzzle in 1848.

17. Who published the first solution of the eight queens puzzle? a) Franz Nauck b) Max Bezzel c) Carl d) Friedrich Answer: a Explanation: The first solution to the Eight Queen Puzzle was given by Franz Nauck in 1850. While the first Eight Queen Puzzle was published by Max Friedrich William Bezzel, who was a German chess composer.

18. When was the first solution to Eight Queen Puzzle published? a) 1850 b) 1847 c) 1848 d) 1849 Answer: a Explanation: The first solution to the Eight Queen Puzzle was given by Franz Nauck in 1850. Max Friedrich William Bezzel, who was a German chess composer by profession published the puzzle in 1848.

19. Who published the extended version of eight queens puzzle? a) Franz Nauck b) Max Bezzel c) Carl d) Friedrich Answer: a Explanation: The first extended version to the Eight Queen Puzzle was given by Franz Nauck in 1850. Max Friedrich William Bezzel published the puzzle in 1848.

20. For how many queens was the extended version of Eight Queen Puzzle applicable for n*n squares? a) 5 b) 6 c) 8 d) n Answer: d Explanation: The extended version given by Franz Nauck of the Eight Queen Puzzle was for n queens on n*n square chessboard. Earlier the puzzle was proposed with 8 queens on 8*8 board. Crack Job Placement Aptitude in First Attempt Prepare for Aptitude with 50+ Videos Lectures and Handmade Notes Click Here! 21. Who was the first person to find the solution of Eight Queen Puzzle using determinant? a) Max Bezzel b) Frank Nauck c) Gunther d) Friedrich Answer: c Explanation: S. Gunther was the first person to propose a solution to the eight queen puzzle using determinant. Max Friedrich William Bezzel published the puzzle and the first solution to the Eight Queen Puzzle was given by Franz Nauck.

22. Who proposed the depth first backtracking algorithm? a) Edsger Dijkshtra b) Max Bezzel c) Frank Nauck d) Carl Friedrich Answer: a Explanation: In 1972, depth first backtracking algorithm was proposed by Edsger Dijkshtra to illustrate the Eight Queen Puzzle. Max Friedrich William Bezzel published the puzzle and the first solution to the Eight Queen Puzzle was given by Franz Nauck.

23. How many solutions are there for 8 queens on 8*8 board? a) 12 b) 91 c) 92 d) 93 Answer: c Explanation: For 8*8 chess board with 8 queens there are total of 92 solutions for the puzzle. There are total of 12 fundamental solutions to the eight queen puzzle.

24. Who publish the bitwise operation method to solve the eight queen puzzle? a) Zongyan Qiu b) Martin Richard c) Max Bezzel d) Frank Nauck Answer: a Explanation: The first person to publish the bitwise operation method to solve the eight queen puzzle was Zongyan Qiu. After him, it was published by Martin Richard.

25. How many fundamental solutions are there for the eight queen puzzle? a) 92 b) 10 c) 11 d) 12 Answer: d Explanation: There are total of 12 fundamental solutions to the eight queen puzzle after removing the symmetrical solutions due to rotation. For 8*8 chess board with 8 queens there are total of 92 solutions for the puzzle. Crack Job Placement Aptitude in First Attempt Prepare for Aptitude with 50+ Videos Lectures and Handmade Notes Click Here! 26. Is it possible to have no four queens in a straight line as the part of one of the solution to the eight queen puzzle. a) True b) False Answer: b Explanation: No three queens lie in a straight line in one of the fundamental solution of the eight queen puzzle.

27. How many fundamental solutions are the for 3 queens on a 3*3 board? a) 1 b) 12 c) 3 d) 0 Answer: d Explanation: There are in total zero solution to the 3 queen puzzle for 3*3 chess board. Hence there are no fundamental solutions. For 8*8 chess board with 8 queens there are total of 12 fundamental solutions for the puzzle.

28. The six queen puzzle has a fewer solution than the five queen puzzle. a) True b) False Answer: a Explanation: There are total 4 solutions for the six queen puzzle and one fundamental solution while there are total of 10 solutions for 5 queen puzzle and 2 fundamental solutions.

29. Which ordered board is the highest enumerated board till now? a) 25*25 b) 26*26 c) 27*27 d) 28*28 Answer: c Explanation: The 27*27 board has the highest order board with total 234,907,967,154,122,528 solutions. It also has total of 29,363,495,934,315,694 fundamental solution.

30. In how many directions do queens attack each other? a) 1 b) 2 c) 3 d) 4 Answer: c Explanation: Queens attack each other in three directions- vertical, horizontal and diagonal. Learn Machine Learning with Python from Scratch Start your Machine learning & Data Science journey with Complete Hands-on Learning & doubt solving Support Click Here! 31. Placing n-queens so that no two queens attack each other is called? a) n-queen’s problem b) 8-queen’s problem c) Hamiltonian circuit problem d) subset sum problem Answer: a Explanation: Placing n queens so that no two queens attack each other is n-queens problem. If n=8, it is called as 8-queens problem.

32. Where is the n-queens problem implemented? a) carom b) chess c) ludo d) cards Answer: b Explanation: N-queens problem occurs in chess. It is the problem of placing n- queens in a n*n chess board.

33. Not more than 2 queens can occur in an n-queens problem. a) true b) false Answer: b Explanation: Unlike a real chess game, n-queens occur in a n-queen problem since it is the problem of dealing with n-queens.

34. In n-queen problem, how many values of n does not provide an optimal solution? a) 1 b) 2 c) 3 d) 4 Answer: b Explanation: N-queen problem does not provide an optimal solution of only three values of n (i.e.) n=2,3.

35. Which of the following methods can be used to solve n-queen’s problem? a) greedy algorithm b) divide and conquer c) iterative improvement d) backtracking Answer: d Explanation: Of the following given approaches, n-queens problem can be solved using backtracking. It can also be solved using branch and bound. Crack Job Placement Aptitude in First Attempt Prepare for Aptitude with 50+ Videos Lectures and Handmade Notes Click Here! 36. Of the following given options, which one of the following is a correct option that provides an optimal solution for 4-queens problem? a) (3,1,4,2) b) (2,3,1,4) c) (4,3,2,1) d) (4,2,3,1) Answer: a Explanation: Of the following given options for optimal solutions of 4-queens problem, (3, 1, 4, 2) is the right option.

37. How many possible solutions exist for an 8-queen problem? a) 100 b) 98 c) 92 d) 88 Answer: c Explanation: For an 8-queen problem, there are 92 possible combinations of optimal solutions.

38. How many possible solutions occur for a 10-queen problem? a) 850 b) 742 c) 842 d) 724 Answer: d Explanation: For a 10-queen problem, 724 possible combinations of optimal solutions are available.

39. If n=1, an imaginary solution for the problem exists. a) true b) false Answer: b Explanation: For n=1, the n-queen problem has a trivial and real solution and it is represented byn-queens-problem-questions-answers-q10

40. What is the domination number for 8-queen’s problem? a) 8 b) 7 c) 6 d) 5 Answer: d Explanation: The minimum number of queens needed to occupy every square in n-queens problem is called domination number. While n=8, the domination number is 5. Python Programming for Complete Beginners Start your Programming Journey with Python Programming which is Easy to Learn and Highly in Demand Click Here! 41. Of the following given options, which one of the following does not provides an optimal solution for 8-queens problem? a) (5,3,8,4,7,1,6,2) b) (1,6,3,8,3,2,4,7) c) (4,1,5,8,6,3,7,2) d) (6,2,7,1,4,8,5,3) Answer: b Explanation: The following given options for optimal solutions of 8-queens problem, (1,6,3,8,3,2,4,7) does not provide an optimal solution.

42. Under what condition any set A will be a subset of B? a) if all elements of set B are also present in set A b) if all elements of set A are also present in set B c) if A contains more elements than B d) if B contains more elements than A Answer: b Explanation: Any set A will be called a subset of set B if all elements of set A are also present in set B. So in such a case set A will be a part of set B.

43. What is a subset sum problem? a) finding a subset of a set that has sum of elements equal to a given number b) checking for the presence of a subset that has sum of elements equal to a given number and printing true or false based on the result c) finding the sum of elements present in a set d) finding the sum of all the subsets of a set Answer: b Explanation: In subset sum problem check for the presence of a subset that has sum of elements equal to a given number. If such a subset is present then we print true otherwise false.

44. Which of the following is true about the time complexity of the recursive solution of the subset sum problem? a) It has an exponential time complexity b) It has a linear time complexity c) It has a logarithmic time complexity d) it has a time complexity of O(n2) Answer: a Explanation: Subset sum problem has both recursive as well as dynamic programming solution. The recursive solution has an exponential time complexity as it will require to check for all subsets in worst case.

45. What is the worst case time complexity of dynamic programming solution of the subset sum problem(sum=given subset sum)? a) O(n) b) O(sum) c) O(n2) d) O(sum*n) Answer: d Explanation: Subset sum problem has both recursive as well as dynamic programming solution. The dynamic programming solution has a time complexity of O(n*sum) as it as a nested loop with limits from 1 to n and 1 to sum respectively. Crack Job Placement Aptitude in First Attempt Prepare for Aptitude with 50+ Videos Lectures and Handmade Notes Click Here! 46. Subset sum problem is an example of NP-complete problem. a) true b) false Answer: a Explanation: Subset sum problem takes exponential time when we implement a recursive solution. Subset sum problem is known to be a part of NP complete problems.

47. Recursive solution of subset sum problem is faster than dynamic problem solution in terms of time complexity. a) true b) false Answer: b Explanation: The recursive solution to subset sum problem takes exponential time complexity whereas the dynamic programming solution takes polynomial time complexity. So dynamic programming solution is faster in terms of time complexity.

48. Which of the following is not true about subset sum problem? a) the recursive solution has a time complexity of O(2n) b) there is no known solution that takes polynomial time c) the recursive solution is slower than dynamic programming solution d) the dynamic programming solution has a time complexity of O(n log n) Answer: d Explanation: Recursive solution of subset sum problem is slower than dynamic problem solution in terms of time complexity. Dynamic programming solution has a time complexity of O(n*sum).

49. Which of the following should be the base case for the recursive solution of subset sum problem? a)

if(sum==0) return true; b)

if(sum==0) return true; if (n ==0 && sum!= 0) return false; c)

if (n ==0 && sum!= 0) return false; d)

if(sum<0) return true; if (n ==0 && sum!= 0) return false; Answer: b Explanation: The base case condition defines the point at which the program should stop recursion. In this case we need to make sure that, the sum does not become 0 and there should be elements left in our array for recursion to happen.

50. What will be the output for the following code? #include <stdio.h> bool func(int arr[], int n, int sum) { if (sum == 0) return true; if (n == 0 && sum != 0) return false; if (arr[n-1] > sum) return func(arr, n-1, sum);

return func(arr, n-1, sum) || func(arr, n-1, sum-arr[n-1]); } int main() { int arr[] = {4,6, 12, 2}; int sum = 12; int n = sizeof(arr)/sizeof(arr[0]); if (func(arr, n, sum) == true) printf(“true”); else printf(“false”); return 0; } a) 12 b) 4 6 2 c) True d) False Answer: c Explanation: The given code represents the recursive approach of solving the subset sum problem. The output for the code will be true if any subset is found to have sum equal to the desired sum, otherwise false will be printed. Crack Job Placement Aptitude in First Attempt Prepare for Aptitude with 50+ Videos Lectures and Handmade Notes Click Here! 51. What will be the output for the following code? #include <stdio.h> bool func(int arr[], int n, int sum) { bool subarr[n+1][sum+1]; for (int i = 0; i <= n; i++) subarr[i][0] = true; for (int i = 1; i <= sum; i++) subarr[0][i] = false; for (int i = 1; i <= n; i++) { for (int j = 1; j <= sum; j++) { if(j<arr[i-1]) subarr[i][j] = subarr[i-1][j]; if (j >= arr[i-1]) subarr[i][j] = subarr[i-1][j] || subarr[i – 1][j-arr[i-1]]; } } return subarr[n][sum]; }

int main() { int arr[] = {3, 3, 4, 4, 7}; int sum = 5; int n = sizeof(arr)/sizeof(arr[0]); if (func(arr, n, sum) == true) printf(“true”); else printf(“false”); return 0; } a) true b) false c) 0 d) error in code Answer: b Explanation: The given code represents the dynamic programming approach of solving the subset sum problem. The output for the code will be true if any subset is found to have sum equal to the desired sum, otherwise false will be printed.

52. What will be the worst case time complexity for the following code? #include <stdio.h> bool func(int arr[], int n, int sum) { if (sum == 0) return true; if (n == 0 && sum != 0) return false; if (arr[n-1] > sum) return func(arr, n-1, sum); return func(arr, n-1, sum) || func(arr, n-1, sum-arr[n-1]); } int main() { int arr[] = {4,6, 12, 2}; int sum = 12; int n = sizeof(arr)/sizeof(arr[0]); if (func(arr, n, sum) == true) printf(“true”); else printf(“false”); return 0; } a) O(n log n) b) O(n2) c) O(2n) d) O(n2 log n) Answer: c Explanation: The given code represents the recursive approach solution of the subset sum problem. It has an exponential time complexity as it will require to check for all subsets in the worst case. It is equal to O(2n).

53. Branch and bound is a __________ a) problem solving technique b) data structure c) sorting algorithm d) type of tree Answer: a Explanation: Branch and bound is a problem solving technique generally used for solving combinatorial optimization problems. Branch and bound helps in solving them faster.

53. Which of the following is not a branch and bound strategy to generate branches? a) LIFO branch and bound b) FIFO branch and bound c) Lowest cost branch and bound d) Highest cost branch and bound Answer: d Explanation: LIFO, FIFO and Lowest cost branch and bound are different strategies to generate branches. Lowest cost branch and bound helps us find the lowest cost path.

54. Which data structure is used for implementing a LIFO branch and bound strategy? a) stack b) queue c) array d) linked list Answer: a Explanation: Stack is the data structure is used for implementing LIFO branch and bound strategy. This leads to depth first search as every branch is explored until a leaf node is discovered.

55. Which data structure is used for implementing a FIFO branch and bound strategy? a) stack b) queue c) array d) linked list Answer: b Explanation: Queue is the data structure is used for implementing FIFO branch and bound strategy. This leads to breadth first search as every branch at depth is explored first before moving to the nodes at greater depth. Crack Job Placement Aptitude in First Attempt Prepare for Aptitude with 50+ Videos Lectures and Handmade Notes Click Here! 56. Which data structure is most suitable for implementing best first branch and bound strategy? a) stack b) queue c) priority queue d) linked list Answer: c Explanation: Priority Queue is the data structure is used for implementing best first branch and bound strategy. Dijkstra’s algorithm is an example of best first search algorithm.

57. Which of the following branch and bound strategy leads to breadth first search? a) LIFO branch and bound b) FIFO branch and bound c) Lowest cost branch and bound d) Highest cost branch and bound Answer: b Explanation: LIFO, FIFO and Lowest cost branch and bound are different strategies to generate branches. FIFO branch and bound leads to breadth first search.

58. Which of the following branch and bound strategy leads to depth first search? a) LIFO branch and bound b) FIFO branch and bound c) Lowest cost branch and bound d) Highest cost branch and bound Answer: a Explanation: LIFO, FIFO and Lowest cost branch and bound are different strategies to generate branches. LIFO branch and bound leads to depth first search.

59. Both FIFO branch and bound strategy and backtracking leads to depth first search. a) true b) false Answer: b Explanation: FIFO branch and bound leads to breadth first search. Whereas backtracking leads to depth first search.

60. Both LIFO branch and bound strategy and backtracking leads to depth first search. a) true b) false Answer: a Explanation: Both backtrackings as well as branch and bound are problem solving algorithms. Both LIFO branch and bound strategy and backtracking leads to depth first search. Python Programming for Complete Beginners Start your Programming Journey with Python Programming which is Easy to Learn and Highly in Demand Click Here! 61. Choose the correct statement from the following. a) branch and bound is more efficient than backtracking b) branch and bound is not suitable where a greedy algorithm is not applicable c) branch and bound divides a problem into at least 2 new restricted sub problems d) backtracking divides a problem into at least 2 new restricted sub problems Answer: c Explanation: Both backtracking as well as branch and bound are problem solving algorithms. Branch and bound is less efficient than backtracking. Branch and bound divides a problem into at least 2 new restricted sub problems.

1. What is a Rabin and Karp Algorithm? a) String Matching Algorithm b) Shortest Path Algorithm c) Minimum spanning tree Algorithm d) Approximation Algorithm Answer: a Explanation: The string matching algorithm which was proposed by Rabin and Karp, generalizes to other algorithms and for two-dimensional pattern matching problems.

2. What is the pre-processing time of Rabin and Karp Algorithm? a) Theta(m2) b) Theta(mlogn) c) Theta(m) d) Big-Oh(n) Answer: c Explanation: The for loop in the pre-processing algorithm runs for m(length of the pattern) times. Hence the pre-processing time is Theta(m).

3. Rabin Karp Algorithm makes use of elementary number theoretic notions. a) True b) False Answer: a Explanation: Rabin Karp Algorithm makes use of elementary theoretic number notions such as the equivalence of two numbers modulo a third number.

4. What is the basic formula applied in Rabin Karp Algorithm to get the computation time as Theta(m)? a) Halving rule b) Horner’s rule c) Summation lemma d) Cancellation lemma Answer: b Explanation: The pattern can be evaluated in time Theta(m) using Horner’s rule: p = P[m] + 10(P[m-1] + 10(P[m-2] +…+ 10(P[2]+10P[1])…)).

5. What is the worst case running time of Rabin Karp Algorithm? a) Theta(n) b) Theta(n-m) c) Theta((n-m+1)m) d) Theta(nlogm) Answer: c Explanation: The worst case running time of Rabin Karp Algorithm is Theta(n-m+1)m). We write Theta(n-m+1) instead of Theta(n-m) because there are n-m+1 different values that the given text takes on.

6. Rabin- Karp algorithm can be used for discovering plagiarism in a sentence. a) True b) False Answer: a Explanation: Since Rabin-Karp algorithm is a pattern detecting algorithm in a text or string, it can be used for detecting plagiarism in a sentence.

7. If n is the length of text(T) and m is the length of the pattern(P) identify the correct pre-processing algorithm. (where q is a suitable modulus to reduce the complexity) p=0; t0=0; a)

for i=1 to n do t0=(dt0 + P[i])mod q p=(dp+T[i])mod q b)

for i=1 to n do p=(dp + P[i])mod q t0=(dt0+T[i])mod q c)

for i=1 to m do t0=(dp + P[i])mod q p=(dt0+T[i])mod q d)

for i=1 to m do p=(dp + P[i])mod q t0=(dt0+T[i])mod q Answer: d Explanation: The pre-processing algorithm runs m (the length of pattern) times. This algorithm is used to compute p as the value of P[1….m] mod q and t0 as the value of T[1….m]mod q.

8. If n is the length of text(T) and m is the length of the pattern(P) identify the correct matching algorithm. a)

for s=0 to n do if p=t0 then if P[1..m]=T[s+1..s+m] then print “Pattern occurs with shift” s b)

for s=0 to n-m do if p=ts then if P[1..m]=T[s+1..s+m] then print “Pattern occurs with shift” s c)

for s=0 to m do if p=ts then if P[1..m]=T[s+1..s+m] then print “Pattern occurs with shift” s d)

for s=0 to n-m do if p!=ts then if P[1..m]=T[s+1..s+m] then print “Pattern occurs with shift” s

Answer: b Explanation: The matching algorithm runs for n-m times. Rabin Karp algorithm explicitly verifies every valid shift. If the required pattern matches with the given text then the algorithm prints pattern found as result.

9. What happens when the modulo value(q) is taken large? a) Complexity increases b) Spurious hits occur frequently c) Cost of extra checking is low d) Matching time increases Answer: c Explanation: If the modulo value(q) is large enough then the spurious hits occur infrequently enough that the cost of extra checking is low.

10. Given a pattern of length-5 window, find the suitable modulo value. 4 3 2 5 0 a) 13 b) 14 c) 12 d) 11 Answer: a Explanation: The modulus q is typically chosen as a prime number that is large enough to reduce the complexity when p is very large. Crack Job Placement Aptitude in First Attempt Prepare for Aptitude with 50+ Videos Lectures and Handmade Notes Click Here! 11. Given a pattern of length- 5 window, find the valid match in the given text. Pattern: 2 1 9 3 6 Modulus: 21 Index: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 Text: 9 2 7 2 1 8 3 0 5 7 1 2 1 2 1 9 3 6 2 3 9 7 a) 11-16 b) 3-8 c) 13-18 d) 15-20 Answer: c Explanation: The pattern 2 1 9 3 6 occurs in the text starting from position 13 to 18. In the given pattern value is computed as 12 by having the modulus as 21. The same text string values are computed for each possible position of a 5 length window.

12. Given a pattern of length- 5 window, find the spurious hit in the given text string. Pattern: 3 1 4 1 5 Modulus: 13 Index: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Text: 2 3 5 9 0 2 3 1 4 1 5 2 6 7 3 9 9 2 1 3 9 a) 6-10 b) 12-16 c) 3-7 d) 13-17 Answer: d Explanation: The sub string in the range 13-17, 6 7 3 9 9 produces the same value 7 as the given pattern. But the pattern numbers don’t match with sub string identified, hence it is a spurious hit.

13. If the expected number of valid shifts is small and modulus is larger than the length of pattern what is the matching time of Rabin Karp Algorithm? a) Theta(m) b) Big-Oh(n+m) c) Theta(n-m) d) Big-Oh(n) Answer: b Explanation: When the number of valid shifts(v) is Big-Oh(1) and q>=m then the matching time is given by O(n)+O(m(v+n/q)) is simplified as O(n+m).

14. What is the basic principle in Rabin Karp algorithm? a) Hashing b) Sorting c) Augmenting d) Dynamic Programming Answer: a Explanation: The basic principle employed in Rabin Karp algorithm is hashing. In the given text every substring is converted to a hash value and compared with the hash value of the pattern.

15. Who created the Rabin Karp Algorithm? a) Joseph Rabin and Michael Karp b) Michael Rabin and Joseph Karp c) Richard Karp and Michael Rabin d) Michael Karp and Richard Rabin Answer: c Explanation: Rabin Karp algorithm was invented by Richard Karp and Michael Rabin in the year 1987 for searching a pattern in the given string. Crack Job Placement Aptitude in First Attempt Prepare for Aptitude with 50+ Videos Lectures and Handmade Notes Click Here! 16. Which of the following is the fastest algorithm in string matching field? a) Boyer-Moore’s algorithm b) String matching algorithm c) Quick search algorithm d) Linear search algorithm Answer: c Explanation: Quick search algorithm is the fastest algorithm in string matching field whereas Linear search algorithm searches for an element in an array of elements.

17. Which of the following algorithms formed the basis for the Quick search algorithm? a) Boyer-Moore’s algorithm b) Parallel string matching algorithm c) Binary Search algorithm d) Linear Search algorithm Answer: a Explanation: Quick search algorithm was originally formed to overcome the drawbacks of Boyer-Moore’s algorithm and also for increased speed and efficiency.

18. What is the time complexity of the Quick search algorithm? a) O(n) b) O(log n) c) O(m+n) d) O(mn) Answer: c Explanation: The time complexity of the Quick search algorithm was found to be O(m+n) and is proved to be faster than Boyer-Moore’s algorithm.

19. What character shift tables does quick search algorithm use? a) good-character shift tables b) bad-character shift tables c) next-character shift tables d) both good and bad character shift tables Answer: b Explanation: Quick search algorithm uses only bad character shift tables and it is one of the reasons for its increased speed than Boyer-Moore’s algorithm.

20. What is the space complexity of quick search algorithm? a) O(n) b) O(log n) c) O(m+n) d) O(mn) Answer: a Explanation: The space complexity of quick search algorithm is mathematically found to be O(n) where n represents the input size. Learn Machine Learning with Python from Scratch Start your Machine learning & Data Science journey with Complete Hands-on Learning & doubt solving Support Click Here! 21. Quick search algorithm starts searching from the right most character to the left. a) true b) false Answer: b Explanation: Quick search algorithm starts searching from the left most character to the right and it uses only bad character shift tables.

22. What character shift tables does Boyer-Moore’s search algorithm use? a) good-character shift tables b) bad-character shift tables c) next-character shift tables d) both good and bad character shift tables Answer: d Explanation: Boyer-Moore’s search algorithm uses both good and bad character shift tables whereas quick search algorithm uses only bad character shift tables.

23. What is the worst case running time in searching phase of Boyer-Moore’s algorithm? a) O(n) b) O(log n) c) O(m+n) d) O(mn) Answer: d Explanation: If the pattern occurs in the text, the worst case running time of Boyer-Moore’s algorithm is found to be O(mn).

24. The searching phase in quick search algorithm has good practical behaviour. a) true b) false Answer: a Explanation: During the searching phase, the comparison between pattern and text characters can be done in any order. It has a quadratic worst case behaviour and good practical behaviour.

25. Given input string = “ABCDABCATRYCARCABCSRT” and pattern string = “CAT”. Find the first index of the pattern match using quick search algorithm. a) 2 b) 6 c) 11 d) 14 Answer: b Explanation: By using quick search algorithm, the given input text string is preprocessed and starts its search from the left most character and finds the first occurrence of the pattern at index=2. Python Programming for Complete Beginners Start your Programming Journey with Python Programming which is Easy to Learn and Highly in Demand Click Here! 26. What will be the output of the following code?

#include<bits/stdc++.h> using namespace std;

void func(char* str2, char* str1) { int m = strlen(str2); int n = strlen(str1); for (int i = 0; i <= n – m; i++) { int j;

for (j = 0; j < m; j++) if (str1[i + j] != str2[j]) break;

if (j == m) cout << i << endl; } }

int main() { char str1[] = “1253234”; char str2[] = “323”; func(str2, str1); return 0; } a) 1 b) 2 c) 3 d) 4 Answer: c Explanation: The given code describes the naive method of finding a pattern in a string. So the output will be 3 as the given sub string begins at that index in the pattern.

27. What will be the worst case time complexity of the following code? #include<bits/stdc++.h> using namespace std;

int main() { char str1[] = “1253234”; char str2[] = “323”; func(str2, str1); return 0; } a) O(n) b) O(m) c) O(m * n) d) O(m + n) Answer: c Explanation: The given code describes the naive method of pattern searching. By observing the nested loop in the code we can say that the time complexity of the loop is O(m*n).

28. What will be the auxiliary space complexity of the following code? #include<bits/stdc++.h> using namespace std;

int main() { char str1[] = “1253234”; char str2[] = “323”; func(str2, str1); return 0; } a) O(n) b) O(1) c) O(log n) d) O(m) Answer: b Explanation: The given code describes the naive method of pattern searching. Its auxiliary space requirement is O(1).

29. What is the worst case time complexity of KMP algorithm for pattern searching (m = length of text, n = length of pattern)? a) O(n) b) O(n*m) c) O(m) d) O(log n) Answer: c Explanation: KMP algorithm is an efficient pattern searching algorithm. It has a time complexity of O(m) where m is the length of text.

30. What will be the best case time complexity of the following code? #include<bits/stdc++.h> using namespace std; void func(char* str2, char* str1) { int m = strlen(str2); int n = strlen(str1);

for (int i = 0; i <= n – m; i++) { int j;

int main() { char str1[] = “1253234”; char str2[] = “323”; func(str2, str1); return 0; } a) O(n) b) O(m) c) O(m * n) d) O(m + n) Answer: b Explanation: The given code describes the naive method of pattern searching. The best case of the code occurs when the first character of the pattern does not appear in the text at all. So in such a case, only one iteration is required thus time complexity will be O(m). Crack Job Placement Aptitude in First Attempt Prepare for Aptitude with 50+ Videos Lectures and Handmade Notes Click Here! 31. What is the time complexity of Z algorithm for pattern searching (m = length of text, n = length of pattern)? a) O(n + m) b) O(m) c) O(n) d) O(m * n) Answer: a Explanation: Z algorithm is an efficient pattern searching algorithm as it searches the pattern in linear time. It has a time complexity of O(m + n) where m is the length of text and n is the length of the pattern.

32. What is the auxiliary space complexity of Z algorithm for pattern searching (m = length of text, n = length of pattern)? a) O(n + m) b) O(m) c) O(n) d) O(m * n) Answer: b Explanation: Z algorithm is an efficient pattern searching algorithm as it searches the pattern in linear time. It an auxiliary space of O(m) for maintaining Z array.

33. The naive pattern searching algorithm is an in place algorithm. a) true b) false Answer: a Explanation: The auxiliary space complexity required by naive pattern searching algorithm is O(1). So it qualifies as an in place algorithm.

34. Rabin Karp algorithm and naive pattern searching algorithm have the same worst case time complexity. a) true b) false Answer: a Explanation: The worst case time complexity of Rabin Karp algorithm is O(m*n) but it has a linear average case time complexity. So Rabin Karp and naive pattern searching algorithm have the same worst case time complexity.

35. The searching phase in quick search algorithm has good practical behaviour. a. true b. false Answer a true

36. If the expected number of valid shifts is small and modulus is larger than the length of pattern what is the matching time of Rabin Karp Algorithm? a. Theta(m) b. Big-Oh(n+m) c. Theta(n-m) d. Big-Oh(n) Answer d

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