algorithms for problem solving mcq

A Basic Quiz on Algorithms #1

This is a set of simple multiple choice questions, provided entirely for your self-assessment, and is based on the most fundamental aspects of data structure and algorithms. The level of the questions is no more than that of what one would encounter in an introductory Programming and Data Structures class in the freshman year at college.

Question 1.

Which of these is the Worst-case time complexity of Quick Sort - and cannot be expressed in lower order terms ?

(a) O(n) (b) O(n log n) (c) O(n 2 ) (d) O(n 3 ) (e) O(log n)

Question 2.

Which of these is the worst case time complexity of Merge Sort - and cannot be expressed in lower order terms ? (a) O(n) (b) O(n log n) (c) O(n 2 ) (d) O(n 3 ) (e) O(log n)

Question 3.

Which of these is the average case time complexity of Merge Sort - and cannot be expressed in lower order terms ?

Question 4.

Which of these is the time complexity involved in building a heap of n elements - and cannot be expressed in lower order terms ?

Question 5.

A heap is a particular kind of a binary search tree. This statement is: (a) True (b) False

Question 6.

The Floyd-Warshall all-pairs shortest path algorithm for finding the shortest distances between nodes in a graph is an example of: (a) A Dynamic Programming formulation. (b) A Greedy Algorithm (c) A recursion based divide and conquer technique.

Question 7.

A bitwise operation 'f' has an interesting characteristic, such that, if f(a,b) = c, it always turns out to be the case that f(b,a) = c; f(a,c) = b; f(c,a) = b; f(b,c) = a; f(c,b) = a. Which of these functions could 'f' possibly be? (a) f(a,b) = a XOR b (b) f(a,b) = a + b (c) f(a,b) = a - b (d) f(a,b) = a * b

( where a and b are the binary representations of any two non-negative integers )

Question 8.

A union find data-structure is commonly applied while implementing: (a) A depth-first search traversal of a graph. (b) A breadth-first search traversal of a graph. (c) Computing the minimum spanning tree of a graph using the Kruskal algorithm. (d) Computing the all-pairs shortest path in a graph.

Question 9.

Which of these is the worst case time complexity for looking up a key in a binary search tree - and cannot be expressed in lower order terms ?

Question 10.

The graph algorithm which forms an essential component of the 'make' or 'ant build' used by programmers and software developers is:

(a) Flow maximization algorithm (b) Shortest path algorithm (c) Minimum spanning tree algorithm (d) Bipartite matching (e) Topological sort

Answer Format

Replace the blanks (represented by three consecutive hyphens) with the appropriate letter ('a','b','c','d' or 'e') for each of the questions. Then, hit the submit button.

Do not add any leading, trailing or intermediate spaces. In case you are unsure about the answer, you can use the letter 'x'. Your answer should contain ten lines, answering sequentially, all of the questions 1 to 10.

For example: (Please note that these are NOT the correct answers and this example is meant only for the purpose of explaining the format)

Explanation

The candidate has concluded that the correct answer the Question 1 is option 'a', the correct answer to Question 2 is option 'b' and so on. He/she does not know the answer to Question 8, hence uses the character 'x'.

Your score is proportional to the number of answers (indicated by the letter options) identified correctly by you.

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• Algorithms MCQ Questions and Answers – Fundamentals – Part 1

C omputer architecture MCQ questions and answers for the preparation of tests, exams, and certifications. So you will find questions about loops and conditionals, data structure, complexity, flowchart, pseudocode, and much more. This systematic learning method will easily prepare anyone to pass their exam.  

1. What is an algorithm?

A A flowchart

B A flowchart or pseudocode

C A decision

D Step by step instructions used to solve a problem

2. What are the three algorithm constructions?

A Input, Output, Process

B Sequence, Selection, Repeat

C Input/Output, Decision, Repeat

D Loop, Input/Output, Process

• Repetition.

3. What is the difference between a flowchart and a pseudocode?

A A flowchart is a diagram while the pseudocode is written in a programming language (e.g. Pascal or Java)

B A flowchart is textual but the pseudocode is a diagram

C A flowchart is a schematic description of an algorithm, while pseudocode is a textual description of an algorithm.

D A flowchart and a pseudocode are the same

4. In a flowchart, an input or output instruction is represented by _____?

A A diamond

B Rectangle

C Parallelogram

5. In a flowchart, a calculation (process) is represented by _____?

6. To repeat a task, we use a ____?

B Condition

7. If ....... then ....... else ....... End If check ____?

A Only one condition

B Two conditions

C Three conditions

D Multiple conditions

8. REPEAT <processing> UNTIL <condition> is a ______?

A Positive loop

The following example uses the REPEAT UNTIL structure to read and validate a positive value:

    9. A flowchart should represent the situation in which, for each grade, a student receives a “Good” or ” Average” the system will consider the grade and if it is equal to or greater than 12, assigns a “Good” grade, otherwise it assigns a “Passable” grade. Which of the following options will be used?   A Input

10. What is a Flowchart?

A A way to design a text-based algorithm

B A specific programming language

C A diagram that represents a set of instructions

D A scheme of instructions

Algorithms MCQ Questions and Answers – Fundamentals – Part 2

Algorithms mcq questions and answers – fundamentals – part 3.

• Algorithms MCQ – Data Structures & Complexity – Part 1
• Algorithms MCQ – Data Structures & Complexity – Part 2

Algorithms MCQ – Data Structures & Complexity – Part 3

• Algorithms MCQ – Data Structures & Complexity – Part 4
• MS Word MCQ Questions and Answers – Part 6

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Computer Algorithms MCQ Questions

• By HJ Penick
• Published April 27, 2018
• Updated December 22, 2023
• 10 mins read

Computer algorithms are a fundamental part of computer programming and problem-solving. They are sets of instructions that solve a specific problem step by step efficiently. Mastering algorithms is essential for any programmer or software engineer. Here are some multiple-choice questions (MCQs) to test your knowledge in computer algorithms.

Key Takeaways

• Computer algorithms are sets of instructions that solve problems efficiently.
• Algorithms are essential for programming and software engineering.
• MCQs are a useful tool for testing knowledge in algorithms.
• Understanding algorithms helps improve problem-solving skills.

1. Which of the following is NOT a characteristic of a good algorithm?

• Flexibility

In computer science, efficiency is a crucial characteristic of a good algorithm as it ensures quick execution and optimal resource usage.

2. What is the time complexity of an algorithm?

• It measures the amount of time it takes to execute an algorithm.
• It determines the space required to store an algorithm’s variables and data structures.
• It expresses the growth rate of an algorithm’s execution time as the input size increases.
• It represents the number of steps an algorithm takes to complete.

The time complexity of an algorithm represents the growth rate of its execution time as the input size increases. It helps estimate the algorithm’s efficiency.

3. Which sorting algorithm has the fastest average-case time complexity?

• Selection Sort
• Insertion Sort

Merge Sort has the fastest average-case time complexity of O(n log n) among the given sorting algorithms.

4. What is the difference between BFS and DFS algorithms?

• BFS explores tree or graph nodes based on their depths, while DFS explores them based on their heights.
• BFS uses a stack data structure, while DFS uses a queue data structure.
• BFS guarantees that it will find the shortest path, while DFS does not.
• BFS is more memory-efficient than DFS.

BFS (Breadth-First Search) explores tree or graph nodes based on their depths, while DFS (Depth-First Search) explores them based on their heights. BFS uses a queue data structure, while DFS uses a stack . DFS does not guarantee finding the shortest path, whereas BFS does.

5. Which algorithm is used for finding the shortest path in a weighted graph?

• Depth-First Search
• Breadth-First Search
• Dijkstra’s Algorithm
• Prim’s Algorithm

Dijkstra’s Algorithm is used for finding the shortest path in a weighted graph. It efficiently solves the single-source shortest path problem.

Frequently Asked Questions (FAQs)

• What is the importance of algorithms in computer programming?

Algorithms are essential in computer programming as they provide systematic and efficient solutions to various problems.

• How can mastering algorithms benefit software engineers?

Mastering algorithms improves problem-solving skills, enhances efficiency in programming, and helps produce optimized software solutions.

Common Misconceptions

1. computer algorithms mcq questions.

There are several misconceptions surrounding computer algorithms MCQ questions. One common misconception is that there is always a single correct answer for each question. However, in reality, multiple choices can be correct based on the specific conditions or requirements of a given problem. Additionally, some may believe that MCQ questions only assess basic knowledge and are not useful for testing higher-level understanding. In truth, well-designed MCQ questions can effectively evaluate critical thinking and problem-solving skills. Lastly, people often think that MCQ questions limit creativity and innovation, but they can be designed to encourage creative thinking and finding novel solutions.

• There can be multiple correct answers in MCQ questions.
• MCQ questions can evaluate critical thinking skills.
• Well-designed MCQ questions can promote creativity and innovation.

2. Application of Algorithms

Another common misconception is that algorithms are only used in computer science or programming-related fields. However, algorithms are widely applicable across various disciplines, including mathematics, engineering, biology, and social sciences. Algorithms are essentially step-by-step procedures to solve problems, making them valuable in any domain that requires problem-solving skills. Another misconception is that algorithms always involve complex mathematical equations or computations. While some algorithms may be mathematically intensive, many algorithms are conceptually simple and can be implemented without extensive mathematical knowledge.

• Algorithms are not limited to computer science or programming fields.
• Algorithm application spans across multiple disciplines.
• Not all algorithms require complex mathematical equations.

3. Efficiency and Optimality

One common misconception is that an efficient algorithm is always the best solution for a problem. While efficiency is important, it is not the only factor to consider. An algorithm’s optimality depends on the specific problem requirements, constraints, and trade-offs. A suboptimal algorithm that is easier to implement or more maintainable may be preferred in certain cases. Moreover, people often assume that an algorithm that works well for one input will work equally well for all inputs. However, the performance of an algorithm can vary based on different input sizes or distributions.

• Efficiency is not the sole factor determining a good algorithm.
• The optimality of an algorithm depends on problem requirements and constraints.
• An algorithm’s performance can vary depending on input characteristics.

4. Algorithm Complexity vs. Difficulty

There is a misconception that algorithm complexity directly correlates with the difficulty of implementing or understanding an algorithm. While complex algorithms can be challenging, simplicity and clarity in algorithm design are crucial for maintainability and comprehension. Clear and well-documented code and algorithmic thinking skills can greatly simplify the implementation and understanding of even complex algorithms. Furthermore, some may assume that a higher-level understanding of mathematics or computer science is necessary to comprehend algorithms. Although knowledge in these disciplines can be helpful, many algorithms can be understood and implemented effectively with a fundamental understanding of problem-solving techniques.

• Algorithm complexity does not determine implementation or understanding difficulty.
• Clear and well-documented code simplifies algorithm implementation.
• Fundamental problem-solving skills are often sufficient for understanding algorithms.

5. Algorithmic Bias and Objectivity

A common misconception is that algorithms are inherently objective and free from bias. However, algorithms are created by humans and can reflect biases present in the data they are trained on or the assumptions made during their design. Biased algorithms can perpetuate discrimination, reinforce stereotypes, or produce unfair outcomes. Additionally, people may think that algorithms always produce deterministic results. While deterministic algorithms always produce the same output for a given input, non-deterministic algorithms like machine learning models can produce varying outputs, leading to different interpretations and decisions.

• Algorithms can have biases inherited from the data or design process.
• Biased algorithms can perpetuate unfair outcomes.
• Not all algorithms produce deterministic results.

Types of Computer Algorithms

There are various types of computer algorithms used in solving different problems. Here are ten different types of algorithms along with a brief description of each:

Sorting Algorithms

Sorting algorithms are used to arrange data in a specific order, such as ascending or descending. Here are some commonly used sorting algorithms:

Search Algorithms

Search algorithms are used to find a particular element in a dataset. These algorithms help in quickly locating a specific item. Here are some popular search algorithms:

Graph Algorithms

Graph algorithms are used to solve problems related to graphs, which consist of nodes and edges. These algorithms help in solving complex network-related problems. Here are some common graph algorithms:

Dynamic Programming Algorithms

Dynamic programming algorithms break down complex problems into smaller subproblems, solving each subproblem only once and storing the result for future use. Here are some examples of dynamic programming algorithms:

Divide and Conquer Algorithms

Divide and conquer algorithms divide a problem into smaller subproblems, solve them separately, and then combine the results to find the final solution. Here are some instances of divide and conquer algorithms:

Greedy Algorithms

Greedy algorithms make locally optimal choices at each step with the hope of finding a global optimum. Here are some examples of greedy algorithms:

Backtracking Algorithms

Backtracking algorithms attempt to solve a problem by exploring all potential solutions incrementally and backtracking when a solution is found to be incorrect. Here are some common backtracking algorithms:

Brute Force Algorithms

Brute force algorithms try all possible solutions to a problem, usually by exhaustively iterating through a large search space. Here are some examples of brute force algorithms:

Randomized Algorithms

Randomized algorithms introduce randomness into the decision-making process, often leading to more efficient solutions. Here are some widely used randomized algorithms:

Parallel Algorithms

Parallel algorithms are designed to perform computations simultaneously on multiple processors or threads, improving efficiency and reducing execution time. Here are some examples of parallel algorithms:

In conclusion, computer algorithms form the backbone of problem-solving in the field of computer science. They help us tackle a wide range of computational problems efficiently and effectively. Whether it’s sorting data, searching for information, or solving complex network-related issues, algorithms play a crucial role in the digital world.

Frequently Asked Questions

What are computer algorithms.

Computer algorithms are step-by-step instructions or procedures designed to solve a specific problem or perform a particular task using a computer system.

Why are algorithms important in computer science?

Algorithms are fundamental to computer science as they provide the foundation for efficient problem-solving and enable the development of software applications.

What is the difference between a recursive and an iterative algorithm?

A recursive algorithm is one that solves a problem by breaking it down into smaller subproblems of the same type, while an iterative algorithm uses loops and iterations to repeatedly execute a set of instructions.

What is the time complexity of an algorithm?

The time complexity of an algorithm is a measure of the amount of time required by an algorithm to run as a function of the input size. It helps analyze the efficiency of an algorithm.

How can algorithm efficiency be measured?

Algorithm efficiency can be measured through various factors such as time complexity, space complexity, and algorithmic analysis techniques like Big O notation.

What is the significance of Big O notation in algorithm analysis?

Big O notation expresses the upper bound of an algorithm’s time complexity in terms of the input size. It allows us to compare algorithms and select the most efficient one for a given problem.

What is a search algorithm?

A search algorithm is used to find a specific element or value in a collection of data. Examples include linear search, binary search, and hash-based search algorithms.

What is a sorting algorithm?

A sorting algorithm is used to arrange a collection of data in a specific order, such as ascending or descending. Common sorting algorithms include bubble sort, quicksort, and merge sort.

What is the difference between a greedy algorithm and a dynamic programming algorithm?

A greedy algorithm makes locally optimal choices at each step to find the overall optimal solution, while a dynamic programming algorithm breaks down a problem into overlapping subproblems and stores the results of subproblems to avoid redundant computation.

How can I learn and practice computer algorithms?

There are several ways to learn and practice computer algorithms, including studying textbooks and online resources, taking algorithmic courses, participating in coding competitions, and implementing algorithms in programming languages.

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Home » MCQs » Computer Science Subjects MCQs

Algorithms MCQs (Multiple-Choice Questions) and Answers

Algorithms are the set of instructions that are used for solving specific tasks. Here, you will find the set of 50+ multiple-choice questions along with the answers and explanations on algorithms. These Algorithms MCQs are written for students as well as professionals to help them prepare for their examinations and interviews. Practice these Algorithms MCQs to learn and enhance your skills in Algorithms .

Algorithms MCQs with Answers and Explanations

Here are the top multiple-choice questions and answers on Algorithms:

1. The primary approach used by the Merge Sort algorithm is ____.

• Dynamic programming
• Greedy approach
• Divide-and-conquer
• Backtracking

Answer: C) Divide-and-conquer

Explanation:

Merge Sort follows the divide-and-conquer approach.

Divide-and-conquer - This approach divides the array into smaller subarrays, sorts each subarray, and then merges them back together.

Discuss this question

2. What is the best-case time complexity of the Merge Sort algorithm?

Answer: B) O(n log n)

Merge Sort has a best-case time complexity of O(n log n), the same as its average and worst-case complexities.

3. Which of the following is a disadvantage of the Merge Sort algorithm?

• It is not stable.
• It has a worst-case time complexity of O(n^2).
• It is not an in-place sorting algorithm.
• It cannot handle large datasets.

Answer: C) It is not an in-place sorting algorithm.

Merge Sort requires additional memory to store the merged subarrays. So, it is not an in-place sorting algorithm.

4. What is the space complexity of Merge Sort?

Answer: C) O(n)

Merge Sort requires additional space proportional to the size of the array being sorted, leading to a space complexity of O(n).

5. Which of the following is a real-world application of Merge Sort?

• Prim's algorithm
• Sorting large datasets
• Depth-first search
• None of the above

Answer: B) Sorting large datasets

Merge Sort is particularly useful for sorting large datasets.

6. What type of graph edges does Dijkstra's Algorithm not handle correctly?

• Parallel edges
• Negative weights
• Zero-weight edges
• All of the above

Answer: C) Zero-weight edges

Dijkstra's Algorithm fails with graphs with negative weight edges because it assumes that once a node's shortest path is found, it cannot be improved, which is not true in the presence of negative weights.

7. The data structure primarily used to implement Dijkstra's Algorithm is ____.

• Priority Queue
• Linked List

Answer: C) Priority Queue

A Priority Queue efficiently extracts the minimum distance vertex and updates the shortest paths in Dijkstra's Algorithm.

8. The best-case time complexity of Dijkstra's Algorithm is ______.

• O(V + E log V)

Answer: D) O(E log V)

When using an adjacency list along with a priority queue, the time complexity of Dijkstra's Algorithm is O (E log V), where:

• E is the number of edges
• V is the number of vertices.

9. One of the best applications of Dijkstra's Algorithm is_____.

• Google Maps
• Predicting weather patterns
• Sorting a list of numbers

Answer: A) Google Maps

Dijkstra's algorithm finds shortest paths in Google Maps.

10. Which of the following problems cannot be solved using Dijkstra's Algorithm?

• Shortest path in a city map
• Optimal network routing
• Handling graphs with negative weight cycles
• Efficient airline route planning

Answer: C) Handling graphs with negative weight cycles

Dijkstra's Algorithm does not work correctly with graphs with negative weight cycles.

11. What is the primary goal of Kruskal's Algorithm?

• To find the shortest path between two vertices
• To find the minimum spanning tree for a connected weighted graph
• To sort all edges of a graph in descending order
• To traverse a graph using a depth-first search

Answer: B) To find the minimum spanning tree for a connected weighted graph

Kruskal's Algorithm is designed to find a subset of the edges that form a tree including every vertex, where the total weight of all the edges in the tree is minimised.

12. In Kruskal's algorithm, first sort all graph edges in increasing order.

• Can't say anything
• Your choice

Answer: A) True

In Kruskal's algo, the first step is sorting all the edges of the graph in increasing order.

13. The time complexity of Kruskal’s algorithm is ____.

• O (E log E)
• O (V log V)

Answer: A) O (E log E)

The time complexity of Kruskal’s algorithm is O (E log E) , where E stands for edges.

14. Kruskal algorithm is considered a _____.

• Greedy algorithm
• Brute force approach
• Two-pointer approach

Answer: A) Greedy algorithm

Kruskal's Algorithm is considered a greedy algorithm because it makes decisions at each step based on the immediate best choice without considering the global optimal solution.

15. What happens if adding an edge creates a cycle in the spanning tree?

• The edge is added to the spanning tree
• The algorithm restarts
• The edge is discarded
• The edge's weight is increased

Answer: C) The edge is discarded

If an edge creates a cycle in the spanning tree, it is discarded to ensure the tree remains acyclic.

16. Which data structure is primarily used in Breadth-First Search (BFS) to keep track of the vertices to be visited?

Answer: B) Queue

BFS uses a queue data structure to maintain the order in which nodes are to be visited.

17. What is the time complexity of Breadth-First Search (BFS) in a graph with 𝑉V vertices and EE edges?

Answer: C) O(V + E)

The time complexity of BFS is O(V + E) because it visits each vertex and each edge once.

18. In BFS, what condition indicates the presence of a cycle in an undirected graph?

• If a node is visited twice during traversal.
• If the graph is connected.
• If the graph has more edges than vertices.
• If a node has no adjacent nodes.

Answer: A) If a node is visited twice during traversal.

In an undirected graph, if BFS encounters a node that has already been visited (and it is not the immediate parent node), it indicates the presence of a cycle.

19. Fill in the blank: BFS is particularly useful for finding the ____ in an unweighted graph.

• Shortest path
• Longest path
• Minimal spanning tree
• Maximum flow

Answer: A) Shortest path

BFS is used to find the shortest path in an unweighted graph because it explores all nodes at the present "depth" level before moving on to nodes at the next depth level.

20. Which of the following is NOT an application of Breadth-First Search (BFS)?

• Shortest path finding in an unweighted graph
• Topological sorting in a directed acyclic graph
• Level order traversal of a binary tree
• Finding the minimum spanning tree

Answer: D) Finding the minimum spanning tree

BFS is not used to find the minimum spanning tree. Algorithms like Kruskal's and Prim's are used for finding the minimum spanning tree.

21. What is the time complexity of the Karatsuba algorithm for multiplying two binary strings?

Answer: B) O(n^1.59)

The Karatsuba algorithm uses divide-and-conquer to reduce the number of required multiplications, leading to an O time complexity (n^1.59).

22. True or False: The Karatsuba algorithm can be applied to any numerical base, not just binary.

The Karatsuba algorithm is a general multiplication technique that can be applied to numbers in any base, though it's often demonstrated with binary numbers.

23. The classical method of binary multiplication has a time complexity of ____.

Answer: B) O(n^2)

The classical method of binary multiplication involves iterating through each bit of the second number and performing shifts and additions, so a time complexity of O(n^2).

24. Which of the following is NOT a step in the Karatsuba algorithm?

• Divide the input strings into halves
• Recursively compute the product of the halves
• Multiply the smaller strings directly without recursion
• Combine the results using shifts and additions

Answer: C) Multiply the smaller strings directly without recursion

The Karatsuba algorithm involves recursive multiplication of the halves. Direct multiplication without recursion is not a part of the Karatsuba method but could be part of the base case handling.

25. The space complexity of the Karatsuba algorithm is _____.

Answer: A) O(n)

The space complexity of the Karastuba algorithm is O(n).

26. What is the primary purpose of the Floyd-Warshall algorithm?

• To find the shortest path from a single source to all other nodes
• To find the shortest path from a single source to a single destination
• To find the shortest paths between all pairs of nodes
• To find the longest paths between all pairs of nodes

Answer: C) To find the shortest paths between all pairs of nodes

The Floyd-Warshall algorithm finds the shortest paths between all pairs of nodes in a weighted graph.

27. The Floyd-Warshall algorithm can handle graphs with which type of edge weights?

• Only positive edge weights
• Only negative edge weights
• Both positive and negative edge weights
• Only zero edge weights

Answer: C) Both positive and negative edge weights

The Floyd-Warshall algorithm can handle graphs with positive and negative edge weights but does not work correctly with graphs containing negative cycles.

28. True or False: The Floyd-Warshall algorithm works for both directed and undirected graphs.

The Floyd-Warshall algorithm can be applied to both directed and undirected weighted graphs.

29. The time complexity of the Floyd-Warshall algorithm is ______.

Answer: C) O(V^3)

The Floyd-Warshall algorithm has a time complexity of O(V^3) due to its three nested loops over the graph vertices.

30. Which of the following statements is true about the Floyd-Warshall algorithm?

• It uses a greedy approach.
• It uses dynamic programming.
• It only works with unweighted graphs.
• It can only find the shortest path for a single source.

Answer: B) It uses dynamic programming.

The Floyd-Warshall algorithm follows a dynamic programming approach to find the shortest paths between all pairs of nodes.

31. Why is the Floyd-Warshall algorithm better suited for dense graphs rather than sparse graphs?

• It has a lower time complexity for dense graphs.
• It runs in constant time for dense graphs.
• It ignores the number of edges in the graph.
• It performs the same regardless of graph density.

Answer: C) It ignores the number of edges in the graph.

The Floyd-Warshall algorithm runs in O(V^3) time regardless of the number of edges.

32. The Floyd-Warshall algorithm does not work correctly for graphs with ______.

• Positive cycles
• Negative cycles
• Directed edges

Answer: C) Negative cycles

The Floyd-Warshall algorithm does not work correctly for graphs containing negative cycles, as the presence of such cycles can lead to incorrect shortest-path calculations.

33. Which of the following is the time complexity of the naive recursive implementation of the LCS problem?

Answer: C) O(2^(m+n))

The naive recursive implementation has an exponential time complexity because it explores all possible subsequences.

34. The Dynamic Programming approach to LCS has an auxiliary space complexity of O(1).

Answer: B) False

The auxiliary space complexity of the standard dynamic programming approach to LCS is O(m * n), where m and n are the lengths of the input strings.

35. The optimal substructure property of the LCS problem indicates that the problem can be broken down into ____.

• Smaller unrelated problems
• Subproblems that can be solved independently
• Overlapping subproblems
• Subproblems that build on the solutions of smaller subproblems

Answer: D) Subproblems that build on the solutions of smaller subproblems

Optimal substructure means the problem can be solved by using the solutions to its subproblems optimally.

36. In the context of the LCS problem, which approach uses a 2D array to store the lengths of common subsequences?

• Greedy Algorithm
• Divide and Conquer
• Dynamic Programming (Bottom-Up)

Answer: C) Dynamic Programming (Bottom-Up)

The bottom-up dynamic programming approach uses a 2D array to store the lengths of common subsequences.

37. What is the auxiliary space complexity of the space-optimized bottom-up approach for LCS?

Answer: B) O(n)

The space-optimized approach uses two arrays of size n, where n is the length of one of the strings.

38. In the LCS problem, if the last characters of the input strings are the same, the LCS length can be derived as 1 + LCS of ____.

• Remaining parts of both strings
• One string with the last character removed and the other string unchanged
• Both strings unchanged
• Remaining part of the first string and unchanged second string

Answer: A) Remaining parts of both strings

If the last characters are the same, we add 1 to the LCS length of the remaining parts of both strings.

39. The LCS length of the strings 'ABC' and 'CBA' is 2?

The LCS length of "ABC" and "CBA" is 1. There are common subsequences of length 1.

40. What type of algorithm is Prim's algorithm?

• Dynamic Programming

Answer: C) Greedy

Prim's algorithm is a greedy algorithm that builds the MST by selecting the minimum weight edges at each step.

41. Prim's algorithm starts with an arbitrary vertex and grows the MST by adding the ____ edge that connects a vertex in the MST to a vertex outside the MST.

Answer: D) Lightest

At each step, Prim's algorithm selects the minimum weight edge that connects the MST to a vertex outside the MST.

42. Prim's algorithm can produce different MSTs depending on the starting vertex?

The choice of the starting vertex can lead to different MSTs, although all will have the same total weight.

43. In Prim's algorithm, what data structure is commonly used to efficiently select the minimum weight edge?

A priority queue helps in efficiently selecting the edge with the minimum weight at each step.

44. The complexity of Prim's algorithm when using an adjacency list and a binary heap is ____.

Answer: B) O(E log V)

When using an adjacency list with a binary heap, Prim's algorithm runs in O(E log V) time.

45. Prim's algorithm guarantees the same MST irrespective of the starting node?

While the total weight of the MST will be the same, the structure of the MST can vary depending on the starting vertex.

46. In Prim's algorithm, what is the initial key value assigned to the starting vertex?

Answer: C) 0

The starting vertex is given a key value of 0 to ensure it is picked first.

47. In the adjacency matrix representation, the time complexity of Prim's algorithm is ____.

Answer: C) O(V^2)

When using an adjacency matrix, the time complexity of Prim's algorithm is O(V^2) due to the need to check all edges for the minimum weight edge selection.

48. What is the primary purpose of Kadane's Algorithm?

• To find the shortest path in a graph
• To find the largest sum of a contiguous subarray
• To sort an array
• To find the minimum sum of a contiguous subarray

Answer: B) To find the largest sum of a contiguous subarray

Kadane's Algorithm is used to find the subarray with the largest sum within a given array.

49. The time complexity of Kadane's algorithm is ____.

Answer: A) 0(n)

The time complexity of Kadane’s algorithm is O(n).

50. For a given array, what will be the maximum contiguous array sum? int n[]={ -2, -3, 4, -1, -2, 1, 5, -3 };

Answer: C) 7

51. Kadane's Algorithm can be used to find the smallest sum contiguous subarray by negating the elements of the array.

By negating all elements in the array, Kadane's Algorithm can be used to find the smallest sum contiguous subarray.

52. Which of the following statements is true about Kadane's Algorithm?

• It finds the maximum sum of any subarray in O(N^2) time.
• It requires additional space proportional to the size of the array.
• It finds the maximum sum of a contiguous subarray in O(N) time.
• It cannot handle arrays with all negative numbers.

Answer: C) It finds the maximum sum of a contiguous subarray in O(N) time.

Kadane's Algorithm efficiently finds the maximum sum of a contiguous subarray with a linear time complexity, O(N).

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Design & Analysis of Algorithms MCQ (Multiple Choice Questions)

Data Structure - II Multiple Choice Questions Highlights

Who should practice data structure - ii mcqs, data structure - ii chapters.

• String Matching
• Number Theory
• Computational Geometry
• Graph Search
• Minimum Spanning Tree
• Shortest Path
• Flow Networks
• Minimum Cut
• Graph Coloring
• Bipartite Graphs
• Greedy Algorithms
• Backtracking
• Dynamic Programming
• Cryptography
• Checksum, Complexity Classes & NP Complete Problems
• Page Replacement Algorithms
• Miscellaneous

1. Searching

The section contains multiple choice questions and answers on linear and binary search iteratives, linear search recursive, jump search, exponential search, uniform binary search and fibonacci search, interpolation and substring searching algorithms.

The section contains questions and answers on sorting techniques like selection sort, bubble sort, merge sort, pancake sort, insertion sort, quicksort, shellsort, heapsort, introsort, timsort, binary tree sort, comb sort, cube sort, cycle sort, library sort, strand sort, cpcktail sort, gnome sort, pigeonhole sort, bogosort, bucket sort, bead sort, stooge sort, recursive bubble sort, tree sort, binary and recursive insertion sort, sleep sort, lsd and msd radix sort, inplace merge sort, bottom-up mergesort, counting sort, odd even and permutation sort.

3. String Matching

The sections contains MCQs on rabin-karp and quick search algorithms.

4. Number Theory

The section contains multiple choice questions and answers on euclids algorithm, strassens algorithm, permutations and combinations generation, partitions and subsets generation, inclusion and exclusion principles.

5. Computational Geometry

The sections contains questions and answers on line point distance, cross product, closest pair problem, quickhull and chan’s algorithm.

6. Graph Search

The sections contains MCQs on depth first search, non recursive dfs, branch and bound, breadth first search and best first search.

7. Minimum Spanning Tree

The section contains multiple choice questions and answers on minimum spanning tree, kruskal’s and prim’s algorithm.

8. Shortest Path

The section contains questions and answers on dijkstra’s algorithm, bellman ford and floyd warshall algorithms.

9. Flow Networks

The sections contains MCQs on maximum flow problem.

10. Matching

The section contains multiple choice questions and answers on stable marriage problem and maximum bipartite matching.

11. Minimum Cut

The sections contains questions and answers on minimum cut.

12. Graph Coloring

The sections contains MCQs on chromatic number, vertex and edge coloring.

13. Bipartite Graphs & Eulerian Tour

The section contains multiple choice questions and answers on complete bipartite graph, bipartite graphs and its properties.

14. Recursion

The section contains questions and answers on recursion, factorial using recursion, fibonacci using recursion, sting reversal using recursion, matrix multiplication, gcd and lcm using recursion, decimal to binary conversions, length of a string, array, linked list using recursion, recursive selection sort, master’s theorem, searching element in array and linkedlist by using recursion.

15. Greedy Algorithms

The sections contains MCQs on fractional knapsack problem, activity selection problem and huffman code.

16. Backtracking

The section contains multiple choice questions and answers on backtracking, eight queens and n queens problem.

17. Dynamic Programming

The sections contains questions and answers on dynamic programming, fibonacci using dynamic programming, coin change problem, kadane algorithm, longest increasing subsequence, rod cutting, minimum no of jumps, 0/1 knapsack problem, matrix chain multiplication, longest common subsequence, edit distance problem, wagner-fischer algorithm, balanced partition, dice throw problem and counting boolean parenthesizations.

18. Cryptography

The sections contains MCQs on monoalphabetic cipher, morse code, polyalphabetic and vigenere cipher, transposition and columnar transposition, pigpen cipher, atbash and gronsfeld cipher, beaufort cipher, autokey and playfair cipher, hill cipher, railfence and route cipher, trithemius cipher, polybius square, running key cipher, bifid and affine cipher.

19. Checksum, Complexity Classes & NP Complete Problems

The section contains multiple choice questions and answers on hamming code, hamiltonian path problem, subset sum and set partition problems, p, np, np-hard and np-complete complexity classes.

20. Page Replacement Algorithms

The section contains questions and answers on optimal page replacement and first in first out algorithm.

21. Miscellaneous

The sections contains MCQs on topological sorting, quickselect, coordinate compression and square root decomposition.

• Searching and Sorting Algorithms in C
• Searching and Sorting in Python
• Searching Algorithms in Java
• Tree Programs in Java
• Sorting Algorithms in Java
• Data Structure MCQ Questions
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Top 50 Data Structures MCQs with Answers

Which of the following sorting algorithms can be used to sort a random linked list with minimum time complexity?

Insertion Sort

In the worst case, the number of comparisons needed to search a singly linked list of length n for a given element is (GATE CS 2002)

log(2*n) -1

Let P be a singly linked list. Let Q be the pointer to an intermediate node x in the list. What is the worst-case time complexity of the best known algorithm to delete the node Q from the list?

What is the worst case time complexity of inserting n elements into an empty linked list, if the linked list needs to be maintained in sorted order ?

Consider the following conditions:

 (a)The solution must be feasible, i.e. it must satisfy all the supply and demand constraints. 

(b)The number of positive allocations must be equal to m1n21, where m is the number of rows and n is the number of columns. 

(c)All the positive allocations must be in independent positions. 

The initial solution of a transportation problem is said to be non-degenerate basic feasible solution if it satisfies: Codes:

(a) and (b) only

(a) and (c) only

(b) and (c) only

(a), (b) and (c)

• In push operation, if new nodes are inserted at the beginning of linked list, then in pop operation, nodes must be removed from end.
• In push operation, if new nodes are inserted at the end, then in pop operation, nodes must be removed from the beginning.
• Both of the above
• None of the above
• Managing function calls
• The stock span problem
• Arithmetic expression evaluation
• All of the above

Question 10

There are 50 questions to complete.

310+ Problem Solving and Python Programming Solved MCQs

• Flow Charts
• Designing Flowcharts
• Pseudo Code
• Numeric Types (Int,Float)
• Precedence of Operators
• Bitwise Operator

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Quantum Physics

Title: solving an industrially relevant quantum chemistry problem on quantum hardware.

Abstract: Quantum chemical calculations are among the most promising applications for quantum computing. Implementations of dedicated quantum algorithms on available quantum hardware were so far, however, mostly limited to comparatively simple systems without strong correlations. As such, they can also be addressed by classically efficient single-reference methods. In this work, we calculate the lowest energy eigenvalue of active space Hamiltonians of industrially relevant and strongly correlated metal chelates on trapped ion quantum hardware, and integrate the results into a typical industrial quantum chemical workflow to arrive at chemically meaningful properties. We are able to achieve chemical accuracy by training a variational quantum algorithm on quantum hardware, followed by a classical diagonalization in the subspace of states measured as outputs of the quantum circuit. This approach is particularly measurement-efficient, requiring 600 single-shot measurements per cost function evaluation on a ten qubit system, and allows for efficient post-processing to handle erroneous runs.

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Algorithm and flowcharts mcqs set-3.

Home » Computer Science MCQs Sets » Algorithm and Flowchart Multiple Choice Questions And Answers (MCQs) » Algorithm and Flowcharts MCQs Set-3

This Algorithm and Flowcharts MCQs contains a carefully curated selection of objective questions, as well as multiple choice questions with answers, sourced from reputable reference books, university exams, and question papers. These resources are invaluable for individuals preparing for university exams,competitive exams and interviews

PRACTICE IT NOW TO SHARPEN YOUR CONCEPT AND KNOWLEDGE

1. Method which uses a list of well defined instructions to complete a task starting from a given initial state from a given initial state to end state is calls as

2. the chart that contains only function flow and no code is called as.

• Structure chart
• Both A and B

3. Which of the following is a program planning tool?

• Pseudo code
• Both B and C

4. Which of the following structures are used in computer programs?

• Timesharing

5. Execution of two or more programs by a single CPU is known as

• Multiprogramming
• Multiprocessing

6. A structured chart is

• A statement of information processing requirements
• A document of what has to be accomplished
• A hierarchical Partitioning of the program
• Beginners all purpose

7. In structure charts modules are described as

8. the sequence logic will not be used while.

• Accepting input from user
• Giving output to the user
• Comparing two sets of data
• Adding two numbers

9. Flowcharts and Algorithms are used for

• Better Programming
• Easy testing and Debugging
• Efficient Coding

10. An Algorithm represented in the form of programming languages is _________

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