algorithms and problem solving mcqs

• 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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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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• Data Structures and Algorithms

Data Structures and Algorithms Multiple Choice Questions (MCQs) and Answers

Master Data Structures and Algorithms with Practice MCQs. Explore our curated collection of Multiple Choice Questions. Ideal for placement and interview preparation, our questions range from basic to advanced, ensuring comprehensive coverage of Data Structures and Algorithms. Begin your placement preparation journey now!

Q1 Which of the following is a characteristic of an efficient algorithm?

Uses minimal CPU time

Uses minimal memory

Is easy to implement

All of the above

Report a Problem!

Q2 The process of breaking a complex problem into smaller, more manageable parts is known as what?

Decomposition

Abstraction

Encapsulation

Inheritance

Q3 What is the primary purpose of pseudocode?

To document algorithms in natural language

To compile and execute algorithms

To debug code

To optimize algorithms

Q4 In algorithm analysis, what does asymptotic complexity refer to?

The complexity in the best case

The complexity in the worst case

The complexity in the average case

The behavior of an algorithm as the input size grows

Q5 What will be the output of the following pseudocode if the input is 5? function factorial(n): if n == 1 return 1 else return n * factorial(n-1)

None of the above

Q6 Consider the following algorithm for calculating the nth Fibonacci number: function fib(n): if n <= 1 return n else return fib(n-1) + fib(n-2). What is the time complexity of this algorithm?

Q7 An algorithm supposed to calculate the sum of numbers from 1 to n returns a higher value than expected. What is the most likely mistake?

Starting the loop from 0

Not initializing the sum variable

Adding n twice

Q8 Given an algorithm that always returns the first element in a sorted list instead of the smallest, what is likely the issue?

The algorithm incorrectly assumes the first element is the smallest

The list is not properly sorted

A loop iterates incorrectly

Q9 Which Big O notation represents constant time complexity?

Q10 For a linear search in an unsorted array of n elements, what is the average case time complexity?

Q11 What does the Big O notation O(n^2) signify about an algorithm's growth rate?

Linear growth

Quadratic growth

Logarithmic growth

Exponential growth

Q12 In Big O notation, what does O(log n) typically represent?

The time complexity of binary search

The time complexity of linear search

The space complexity of sorting algorithms

The space complexity of hashing

Q13 Which of the following best describes the time complexity of inserting an element into a binary search tree?

Q14 What is the worst-case time complexity of quicksort?

Q15 How does the space complexity of an iterative solution compare to a recursive solution for the same problem?

Iterative solutions always use more space

Recursive solutions always use more space

Depends on the specific problem

They use the same amount of space

Q16 What is the time complexity of the following code snippet? for i in range(n): print(i)

Q17 Given the code for i in range(n): for j in range(n): print(i, j), what is the time complexity?

Q18 Analyze the time complexity of the following function: def func(n): if n <= 1: return else func(n/2) + func(n/2)

Q19 An algorithm that should run in O(n log n) time complexity runs significantly slower. The likely cause is:

Incorrect base case in recursion

Excessive memory allocation

Poor choice of pivot in sorting

Q20 A function designed to be O(n) complexity takes longer as n increases. What might be overlooked?

Nested loops

Constant factors

Linear operations

Q21 A recursive algorithm expected to have a time complexity of O(log n) is running slower. The likely issue is:

Not halving the input on each recursive call

Incorrect termination condition

Stack overflow

Q22 Which data structure should be used to store a collection of characters in a sequence?

Q23 What is the time complexity of accessing an element in an array by its index?

Q24 Which of the following is NOT a valid reason to use a string builder in Java instead of concatenating strings using the + operator?

It reduces memory usage

It is faster for concatenating multiple strings

It is immutable

It can be used in multi-threaded environments

Q25 In an unsorted array of integers, what is the best time complexity achievable for searching for a specific value?

Q26 Considering a character array representing a string, what is the space complexity for storing this string?

Q27 Which operation has a worse time complexity in a dynamic array when it needs to expand its size?

Accessing an element by index

Appending an element at the end

Inserting an element at the beginning

Searching for an element

Q28 What does the following Python code snippet return? arr = ['a', 'b', 'c', 'd']; print(arr[1:3])

['b', 'c', 'd']

Q29 Given an array of integers, which of the following operations will NOT mutate the original array in JavaScript?

arr.push(5)

[...arr, 5]

Q30 What is the result of concatenating two arrays in Python using the + operator, arr1 = [1, 2, 3] and arr2 = [4, 5, 6]?

A new array [1, 2, 3, 4, 5, 6]

The original arrays are mutated to include the elements of the other

A syntax error

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Algorithm and flowchart multiple choice questions and answers (mcqs).

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

When practicing Algorithm Flowchart and Control Structure multiple-choice questions (MCQs), students should focus on the following aspects:

1. Understanding concepts:

2. syntax and notation:, 3. identifying correct solutions:, 4. analyzing code behavior:, 5. problem-solving skills:, 6. time complexity and efficiency:.

By practicing these aspects, students can develop a strong foundation in algorithm flowcharts and control structures, which are essential for understanding and designing efficient algorithms. Additionally, practicing MCQs helps students improve their analytical and problem-solving skills, which are valuable in various programming and algorithmic scenarios.

Algorithm and Flowcharts MCQs Set-1

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 Read More »

Algorithm and Flowcharts MCQs Set-2

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

Algorithm and Flowcharts MCQs Set-3

Algorithm and flowcharts mcqs set-4, algorithm and flowcharts mcqs set-5, algorithm and flowcharts mcqs set-6, algorithm and flowcharts mcqs set-7, algorithm and flowcharts mcqs set-8.

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Artificial Intelligence MCQ – Problem-Solving Agents

Here are 25 multiple-choice questions (MCQs) related to Artificial Intelligence, focusing specifically on Problem-Solving Agents. Each question includes four options, the correct answer, and a brief explanation. These MCQ questions cover various aspects of AI problem-solving agents, including algorithms, search strategies, optimization techniques, and problem-solving methods, providing a comprehensive overview of this area in AI.

1. What is the primary objective of a problem-solving agent in AI?

Explanation:.

A problem-solving agent is designed to find a sequence of actions that leads from the initial state to a goal state, solving a specific problem or achieving a set goal.

2. In AI, a heuristic function is used in problem-solving to:

A heuristic function is used to guide the search process by providing an educated guess about the cost to reach the goal from each node, thus helping to efficiently reduce the search space.

3. Which algorithm is commonly used for pathfinding in AI?

The A* Algorithm is widely used for pathfinding and graph traversal. It efficiently finds the shortest path between two nodes in a graph, combining the features of uniform-cost search and greedy best-first search.

4. What is "backtracking" in AI problem-solving?

Backtracking involves going back to previous states and trying different actions when the current path does not lead to a solution, allowing for exploring alternative solutions.

5. The "branch and bound" technique in AI is used to:

Branch and bound is an algorithmic technique used for solving various optimization problems. It systematically enumerates candidate solutions by branching and then uses a bounding function to eliminate suboptimal solutions.

6. Which of the following is a characteristic of a depth-first search algorithm?

Depth-first search explores as far as possible along each branch before backtracking, going deep into a search tree before exploring siblings of earlier nodes.

7. In AI, "constraint satisfaction problems" are typically solved using:

Constraint satisfaction problems, where a set of constraints must be met, are commonly solved using backtracking algorithms, which incrementally build candidates to the solutions and abandon candidates as soon as they determine that the candidate cannot possibly be completed to a valid solution.

8. The primary goal of "minimax" algorithm in AI is:

The minimax algorithm is used in decision-making and game theory to minimize the possible loss for a worst-case scenario. When dealing with gains, it seeks to maximize the minimum gain.

9. What is "state space" in AI problem-solving?

The state space in AI problem-solving refers to the set of all possible states that can be reached from the initial state by applying a sequence of actions. It is often represented as a graph.

10. In AI, "pruning" in the context of search algorithms refers to:

Pruning in search algorithms involves eliminating paths that are unlikely to lead to the goal or are less optimal, thus reducing the search space and improving efficiency.

11. The "traveling salesman problem" in AI is an example of:

The traveling salesman problem is a classic optimization problem in AI and computer science, where the goal is to find the shortest possible route that visits a set of locations and returns to the origin.

12. "Greedy best-first search" in AI prioritizes:

Greedy best-first search is a search algorithm that prioritizes nodes that seem to be leading to a solution the quickest, often using a heuristic to estimate the cost from the current node to the goal.

13. In AI, "dynamic programming" is used to:

Dynamic programming is a method for solving complex problems by breaking them down into simpler subproblems. It is used when the subproblems are overlapping and the problem exhibits the properties of optimal substructure.

14. The "Monte Carlo Tree Search" algorithm in AI is widely used in:

Monte Carlo Tree Search (MCTS) is an algorithm used for making decisions in some kinds of game-playing, particularly where it is impractical to search all possible moves due to the complexity of the game.

15. What does an "admissible heuristic" in AI guarantee?

An admissible heuristic is one that never overestimates the cost to reach the goal. In heuristic search algorithms, using an admissible heuristic guarantees finding an optimal solution.

16. The concept of "hill climbing" in AI problem solving is similar to:

Hill climbing in AI is a mathematical optimization technique which belongs to the family of local search. It is used to solve computational problems by continuously moving in the direction of increasing elevation or value.

17. The "no free lunch theorem" in AI implies that:

The "no free lunch" theorem states that no one algorithm works best for every problem. It implies that each problem needs to be approached uniquely and that there's no universally superior method.

18. In AI, "means-ends analysis" is a technique used in:

Means-ends analysis is a problem-solving technique used in AI that involves breaking down the difference between the current state and the goal state into smaller and smaller differences, then achieving those smaller goals.

19. The "Pigeonhole principle" in AI is used to:

In AI and mathematics, the Pigeonhole principle is used to prove that a solution exists under certain conditions. It states that if n items are put into m containers, with n > m, then at least one container must contain more than one item.

20. "Simulated annealing" in AI is inspired by:

Simulated annealing is an optimization algorithm that mimics the process of annealing in metallurgy. It involves heating and controlled cooling of a material to increase the size of its crystals and reduce their defects.

21. In AI, the "Bellman-Ford algorithm" is used for:

The Bellman-Ford algorithm is an algorithm that computes shortest paths from a single source vertex to all of the other vertices in a weighted graph. It's particularly useful for graphs where edge weights may be negative.

22. What is the primary function of "Alpha-Beta pruning" in AI?

Alpha-Beta pruning is a search algorithm that seeks to decrease the number of nodes that are evaluated by the minimax algorithm in its search tree. It is used in game playing to prune away branches that cannot possibly influence the final decision.

23. The "Hungarian algorithm" in AI is best suited for solving:

The Hungarian algorithm, a combinatorial optimization algorithm, is used for solving assignment problems where the goal is to assign resources or tasks to agents in the most effective way.

24. In problem-solving, "depth-limited search" is used to:

Depth-limited search is a modification of depth-first search, where the search is limited to a specific depth. This prevents the algorithm from going down infinitely deep paths and helps manage the use of memory.

25. "Bidirectional search" in AI problem solving is used to:

Bidirectional search is an efficient search strategy that runs two simultaneous searches: one forward from the initial state and the other backward from the goal, stopping when the two meet. This approach can drastically reduce the amount of required exploration.

Related MCQ (Multiple Choice Questions) :

Artificial intelligence mcq – agents, artificial intelligence mcq – natural language processing, artificial intelligence mcq – partial order planning, artificial intelligence mcq – expert systems, artificial intelligence mcq – fuzzy logic, artificial intelligence mcq – neural networks, artificial intelligence mcq – robotics, artificial intelligence mcq – rule-based system, artificial intelligence mcq – semantic networks, artificial intelligence mcq – bayesian networks, artificial intelligence mcq – alpha beta pruning, artificial intelligence mcq – text mining, leave a comment cancel reply.

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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.