Algorithm Design Techniques: Master the Art of Efficient Algorithm Design

Question 1

Which algorithm employs dynamic programming to store computed results and prevent redundant calculations?

Accepted Answer

Fibonacci number calculation

Answer

Merge sort

Answer

Depth-first search

Answer

Binary search

Question 2

Which of the following is NOT a widely recognized algorithm design technique?

Accepted Answer

Trial and Error

Answer

Divide and Conquer

Answer

Greedy Algorithm

Answer

Dynamic Programming

Question 3

Which optimization technique commonly involves breaking down a problem into smaller, recursive subproblems?

Accepted Answer

Divide and Conquer

Answer

Brute-force

Answer

Dynamic Programming

Answer

Greedy

Question 4

What is a defining characteristic of divide-and-conquer algorithms?

Accepted Answer

They break down problems into smaller ones recursively.

Answer

They always rely on a stack to store subproblems.

Answer

Their time complexity is consistently O(n^2).

Answer

They guarantee finding the best solution every time.

Question 5

Which of the following is a dynamic programming technique used to optimize the solution to a problem by storing the results of subproblems to avoid redundant calculations?

Accepted Answer

Memoization

Answer

Greedy algorithm

Answer

Divide and conquer

Answer

Backtracking

Question 6

Which of these techniques are commonly used in algorithm design?

Accepted Answer

Recursion

Accepted Answer

Dynamic programming

Accepted Answer

Divide and conquer

Answer

Bubble sort

Question 7

Which characteristic is NOT a feature of dynamic programming algorithms?

Accepted Answer

Greedy decision-making

Answer

Optimal substructure

Answer

Overlapping subproblems

Answer

Memoization

Question 8

Which of these algorithms uses a recursive divide-and-conquer approach?

Accepted Answer

Merge Sort

Answer

Breadth-first search

Answer

Binary search

Answer

Dijkstra's algorithm

Question 9

Which of the following techniques is not commonly used in dynamic programming?

Accepted Answer

Greedy algorithm

Answer

Memoization

Answer

Recursion

Answer

Bottom-up approach

Question 10

Which step is NOT part of the divide-and-conquer algorithm design technique?

Accepted Answer

Perform a brute-force search of the solution space.

Answer

Divide the problem into smaller subproblems.

Answer

Solve the subproblems.

Answer

Combine the solutions to the subproblems to solve the original problem.

Question 11

Which of the following is NOT a fundamental algorithm design technique used in computer science?

Accepted Answer

Brute force

Answer

Recursion

Answer

Dynamic programming

Answer

Greedy algorithms

Question 12

Which algorithm among the following does not utilize the divide-and-conquer approach?

Accepted Answer

Breadth-First Search (BFS)

Answer

Merge Sort

Answer

Binary Search

Answer

Quick Sort

Question 13

When should you generally avoid using a 'brute force' approach to problem-solving?

Accepted Answer

When there are many possible solutions or constraints.

Answer

When the problem is small and easy to solve.

Answer

When there are multiple equally good solutions.

Answer

When the problem requires backtracking or recursion.

Question 14

Which algorithm design technique involves breaking down a problem into smaller, independent subproblems that are solved recursively?

Accepted Answer

Divide and Conquer

Answer

Dynamic Programming

Answer

Greedy Algorithms

Answer

Branch and Bound

Question 15

Which algorithm design technique is ideal for efficiently solving problems that can be broken down into smaller, independent subproblems?

Accepted Answer

Divide and Conquer

Answer

Dynamic Programming

Answer

Greedy

Answer

Branch and Bound

Question 16

When solving a problem where optimal solutions to smaller subproblems can be reused to solve larger problems, which algorithm design technique is most suitable?

Accepted Answer

Dynamic Programming

Answer

Greedy

Answer

Divide and Conquer

Answer

Backtracking

Question 17

Which of the following is NOT a core algorithm design technique used to solve complex problems efficiently?

Accepted Answer

Brute Force

Answer

Divide and Conquer

Answer

Recursion

Answer

Dynamic Programming

Question 18

For the 0-1 Knapsack problem, which algorithm design technique is most suitable?

Accepted Answer

Dynamic Programming

Answer

Divide and Conquer

Answer

Greedy Algorithm

Answer

Branch and Bound

Question 19

In approximation algorithms, which technique is used to find solutions within a guaranteed percentage of the optimal solution?

Accepted Answer

Randomized rounding

Answer

Dynamic programming

Answer

Backtracking

Answer

Divide and conquer

Question 20

What is a key benefit of using a divide-and-conquer algorithm?

Accepted Answer

Can solve complex problems by breaking them down into smaller ones.

Answer

Always uses less resources than other algorithms.

Answer

Always finds the best possible solution.

Answer

Can be used to solve any type of problem.

Question 21

What is the defining characteristic of a greedy algorithm?

Accepted Answer

Making localized optimal choices without considering long-term consequences

Answer

Breaking down a problem into smaller parts

Answer

Evaluating all possible solutions to find the best one

Answer

Using randomness to guide the solution search

Question 22

What sets dynamic programming apart from other algorithm design techniques, making it particularly advantageous for solving certain types of problems?

Accepted Answer

Dynamic programming efficiently handles problems with overlapping subproblems, allowing for the reuse of previously computed solutions.

Answer

Dynamic programming guarantees optimal solutions for all problems.

Answer

Dynamic programming is ideal for solving problems with extremely large input sizes.

Answer

Dynamic programming can effectively solve problems with an excessively large number of potential solutions, such as those with exponential complexity.

Question 23

In randomized algorithms, which technique leverages randomness to approximate solutions?

Accepted Answer

Randomized rounding

Answer

Using random numbers to prevent infinite loops

Question 24

Approximation algorithms are used to tackle problems where finding an exact optimal solution is computationally impractical. What is a key characteristic of approximation algorithms?

Accepted Answer

They are often used for NP-hard problems.

Answer

They guarantee exact solutions to problems.

Answer

They always have polynomial time complexity.

Answer

They are inherently randomized algorithms.

Question 25

Which algorithm design technique involves breaking a problem into smaller subproblems and solving them recursively?

Accepted Answer

Divide and Conquer

Answer

Dynamic Programming

Answer

Greedy Algorithms

Question 26

What is a defining characteristic of greedy algorithms?

Accepted Answer

They make choices that seem best at the moment.

Answer

They always find the best possible solution.

Answer

They often require going back and changing previous choices.

Answer

Their running time is always O(n^2).

Question 27

In backtracking algorithms, when is it more appropriate to use a depth-first search (DFS) approach instead of a breadth-first search (BFS) approach?

Accepted Answer

DFS is preferred when the solution space is large and the optimal solution is not expected to be found quickly.

Answer

DFS is preferred when the solution space is large and the optimal solution is expected to be found quickly.

Answer

BFS is preferred when the solution space is small and the optimal solution is not expected to be found quickly.

Answer

BFS is preferred when the solution space is small and the optimal solution is expected to be found quickly.

Question 28

Which characteristic does NOT belong to the divide-and-conquer algorithm design technique?

Accepted Answer

Employing a brute-force approach to solve the subproblems

Answer

Recursively dividing a problem into smaller subproblems

Answer

Solving the subproblems independently

Answer

Combining the solutions to the subproblems to solve the original problem

Question 29

In the divide and conquer approach to solving a problem, what is the key idea that makes it efficient?

Accepted Answer

Breaking the problem into smaller subproblems, solving them separately, and combining their solutions to solve the original problem.

Answer

Repeatedly dividing the problem into smaller subproblems until a simple case is reached.

Answer

Guessing a solution and checking if it meets the problem's requirements.

Answer

Gradually improving an initial solution through iterations to get a satisfactory solution.

Question 30

In branch and bound algorithms, which technique is primarily used to reduce the search space and enhance efficiency?

Accepted Answer

Upper and lower bounds

Answer

Depth-first search

Answer

Breadth-first search

Answer

Dynamic programming

Question 31

In randomized algorithms, which technique is commonly used to enhance efficiency and accuracy?

Accepted Answer

Leveraging random sampling to minimize iterations

Answer

Always choosing the first element as the solution

Answer

Reversing the order of input elements

Answer

Applying a deterministic approach for consistent results

Question 32

Which algorithm design technique is best suited to determine the longest common subsequence between two given strings?

Accepted Answer

Dynamic Programming

Answer

Divide and Conquer

Answer

Greedy Algorithms

Answer

Backtracking

Question 33

Which algorithm design technique breaks a problem into smaller, independent subproblems, solves each subproblem separately, and combines the solutions to solve the original problem?

Accepted Answer

Divide and Conquer

Answer

Greedy Algorithm

Answer

Backtracking

Question 34

Which algorithm design technique involves breaking down a problem into smaller, solvable parts?

Accepted Answer

Divide and Conquer

Answer

Dynamic Programming

Answer

Greedy Algorithm

Question 35

What is a primary advantage of using amortized analysis in algorithm design?

Accepted Answer

It enables efficient analysis of algorithms with unpredictable or complex time behavior.

Answer

It makes worst-case analysis unnecessary.

Answer

It is universally applicable to all algorithms.

Answer

It consistently provides a stricter upper bound on time complexity.

Question 36

Which algorithm design technique prioritizes immediate gains over long-term optimality, making locally optimal choices at each step?

Accepted Answer

Greedy Algorithms

Answer

Divide and Conquer

Answer

Dynamic Programming

Question 37

Which of the following best describes the divide-and-conquer approach in algorithm design?

Accepted Answer

Breaking a problem into smaller subproblems, solving them separately, and combining their solutions to solve the original problem.

Answer

Trying out all possible solutions until the desired outcome is achieved.

Answer

Storing solutions to subproblems to avoid repeating calculations.

Answer

Making locally optimal choices without considering the overall impact.

Question 38

In algorithm design, what's the core principle of the divide-and-conquer approach?

Accepted Answer

Breaking down a problem into smaller, similar subproblems, solving them recursively, and combining the solutions to solve the original problem.

Answer

Organizing data in a hierarchical structure, using tree or linked list data structures for efficient storage and retrieval.

Answer

Making an initial solution guess and iteratively refining it until it approximates the optimal solution.

Answer

Generating candidate solutions randomly and selecting the best one based on a fitness function.

Question 39

In algorithm analysis, which term represents the theoretical limit on the efficiency of solving a problem, irrespective of the algorithm employed?

Accepted Answer

Lower bound

Answer

Best-case analysis

Answer

Average-case analysis

Question 40

To efficiently determine the longest common subsequence (LCS) between two strings, X and Y, which algorithm design technique should be employed?

Accepted Answer

Dynamic Programming

Answer

Divide and Conquer

Answer

Greedy Algorithms

Answer

Backtracking

Question 41

The Master theorem is a technique for analyzing the asymptotic complexity of divide-and-conquer recurrences. What is its main purpose?

Accepted Answer

To categorize recurrences and provide formulas for their asymptotic complexity

Answer

To calculate the exact running time of a recursive algorithm

Answer

To always provide a tight upper bound on the running time of a recursive algorithm

Question 42

Which algorithm design technique involves breaking down a problem into smaller versions of itself, recursively solving each subproblem?

Accepted Answer

Divide and Conquer

Answer

Dynamic Programming

Answer

Greedy Algorithms

Answer

Branch and Bound

Question 43

In a backtracking algorithm, when an unproductive path is encountered, what is the typical action taken to explore alternative solutions?

Accepted Answer

Backtrack to the most recent state with unexplored alternatives.

Answer

Continue on the current path until a solution is found.

Answer

Abandon the current path and restart the search from the beginning.

Question 44

Which of the following algorithms does NOT employ the divide-and-conquer approach?

Accepted Answer

Breadth-first search

Answer

Merge sort

Answer

Binary search

Answer

Quick sort

Question 45

When selecting an algorithm design technique, which factor is NOT a primary consideration?

Accepted Answer

Algorithm's ease of implementation

Answer

Size of the input data set

Answer

Presence of constraints or limitations

Answer

Desired level of accuracy or precision

Question 46

How do the size and characteristics of an input problem impact the time complexity class of an algorithm designed to solve it?

Accepted Answer

Input characteristics can alter the time complexity class of an algorithm.

Answer

Input size only affects the constant factor in time complexity, not the overall class.

Answer

Time complexity class is solely determined by asymptotic behavior, regardless of input.

Question 47

Analyze the ethical considerations involved in using algorithms for decision-making. Discuss the potential advantages, risks, and guidelines for designing ethical algorithms.

Accepted Answer

Ethical considerations should be prioritized during algorithm design to minimize potential harms and ensure responsible use.

Answer

Algorithms can eliminate human bias and always result in fairer outcomes.

Answer

The ethical implications of algorithms are universal and do not vary depending on the context of their use.

Question 48

When is recursion a suitable algorithm design technique?

Accepted Answer

When the problem can be naturally decomposed into smaller, similar subproblems

Answer

When the problem demands a brute-force approach

Answer

When the problem is too intricate to solve iteratively

Answer

Recursion should never be utilized in algorithm design

Question 49

What is the core concept behind the divide-and-conquer algorithm design technique?

Accepted Answer

Decompose the problem into smaller subproblems, solve them independently, and merge the solutions

Answer

Repeatedly break down the problem into smaller and smaller parts

Answer

Utilize recursion to solve the problem by making recursive calls with smaller inputs

Answer

Employ dynamic programming to store subproblem solutions and avoid redundant calculations

Question 50

What is the time complexity of the Fibonacci sequence computed using recursion?

Accepted Answer

O(2^n)

Answer

O(log n)

Answer

O(n)

Answer

O(n^2)

Question 51

Which dynamic programming technique uses a table to store previously computed solutions?

Accepted Answer

Tabulation

Answer

Memoization

Answer

Greedy

Answer

Backtracking

Question 52

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

Accepted Answer

Recursive algorithms use recursion, while iterative algorithms use iteration

Answer

Iterative algorithms store intermediate results, while recursive algorithms do not

Answer

Recursive algorithms are inherently more efficient than iterative algorithms

Answer

There is no fundamental difference between recursive and iterative algorithms

Question 53

What is the time complexity of dynamic programming when addressing the Fibonacci sequence problem using tabulation?

Accepted Answer

O(n)

Answer

O(log n)

Answer

O(n^2)

Answer

O(2^n)

Question 54

How does the choice of algorithm design technique affect an algorithm's ability to handle larger inputs efficiently?

Accepted Answer

Different design techniques have varying time complexities, impacting scalability as input size increases.

Answer

All design techniques result in equally scalable algorithms, regardless of input size.

Answer

Scalability depends solely on the programming language used.

Answer

Input size has no impact on scalability.

Question 55

When faced with a problem involving a sequence of tasks that require optimal scheduling, which algorithm design technique is most appropriate to employ?

Accepted Answer

Dynamic Programming

Answer

Recursion

Answer

Greedy Algorithm

Answer

Divide-and-Conquer

Question 56

In algorithm design, how does the inherent complexity of a problem impact the choice of an appropriate technique?

Accepted Answer

It determines the efficiency and feasibility of the algorithm.

Answer

It has no bearing on the algorithm design process.

Answer

It solely affects the algorithm's space requirements.

Answer

It helps identify the optimal algorithm for solving the problem.

Question 57

Which algorithm design principle promotes maintainability by keeping changes isolated to specific code sections?

Accepted Answer

Encapsulation

Answer

Inheritance

Answer

Polymorphism

Answer

Recursion

Question 58

For situations where a quick, though potentially non-optimal, solution is needed, which algorithm design approach is most appropriate?

Accepted Answer

Greedy

Answer

Divide-and-Conquer

Answer

Dynamic Programming

Question 59

In algorithm design, how should ethical considerations shape the responsibilities of designers?

Accepted Answer

Prioritize social good and minimize potential harm in algorithm design.

Answer

Prioritize technical efficiency without regard for ethical implications.

Answer

Transfer ethical decision-making to users, absolving designers of responsibility.

Answer

Ignore potential biases and maintain objectivity in algorithm development.

Question 60

In algorithm design, how does analyzing time and space complexity influence the selection of data structures and optimization of algorithm performance?

Accepted Answer

Complexity analysis informs the selection of data structures that minimize both time and space complexity.

Answer

Complexity analysis is not relevant to algorithm design choices.

Answer

Only time complexity should be considered, as space complexity is less significant.

Answer

Complexity analysis helps identify algorithms with the lowest possible time complexity, even at the expense of space usage.

Question 61

Which algorithm design technique leverages previously computed values to optimize efficiency?

Accepted Answer

Dynamic Programming

Answer

Divide and Conquer

Answer

Greedy Algorithms

Question 62

In algorithm design, recursion offers the primary advantage of:

Accepted Answer

Decomposing a problem into smaller, manageable subproblems

Answer

Eliminating the need for iterative statements

Answer

Enhancing code readability and maintainability

Answer

Reducing the memory overhead

Question 63

Which of the following problems exemplifies the application of dynamic programming?

Accepted Answer

Computing Fibonacci numbers

Answer

Determining the shortest path in a graph

Answer

Sorting an array of elements

Answer

Searching for an element in a binary tree

Question 64

Among the following, which is NOT an application of dynamic programming?

Accepted Answer

Sorting algorithms

Answer

Sequence alignment in bioinformatics

Answer

Image processing

Answer

Machine learning