What does the Nash equilibrium in game theory represent?
A Optimal decision-making strategy
B Worst-case scenario
C Best payoff for all
D Random decision-making
The Nash equilibrium represents a situation in a game where no player can improve their payoff by changing their strategy, assuming the strategies of others remain unchanged. It is a fundamental concept in non-cooperative games.
What is a key feature of supervised learning in machine learning?
A No labeled data
B No training data required
C Relies on randomization
D Uses feedback to improve
Supervised learning involves training a model on labeled data, where the algorithm learns to map inputs to correct outputs. The model’s predictions are corrected using feedback to improve performance.
What is the main goal of reinforcement learning?
A To maximize cumulative reward
B To minimize loss
C To predict future events
D To classify data
In reinforcement learning, an agent learns to make decisions by performing actions that maximize a cumulative reward over time. It explores the environment, receives feedback, and optimizes its strategy based on that feedback.
What is the time complexity of the Parallel Merge Sort algorithm?
A O(n)
B O(n log n)
C O(n²)
D O(log n)
Parallel Merge Sort divides the data into subarrays, sorts them concurrently, and then merges them. Despite using parallelism to speed up the sorting process, the overall time complexity remains O(n log n), similar to sequential Merge Sort.
Which of these algorithms is commonly used in distributed computing?
A Quick Sort
B Merge Sort
C MapReduce
D Dijkstra’s Algorithm
MapReduce is a widely used algorithm in distributed computing for processing large data sets. It splits the work into two phases—map (distribution) and reduce (aggregation)—and runs across multiple machines to improve performance.
In the context of game theory, what is a dominant strategy?
A The best strategy given the opponent’s choices
B A random strategy
C A strategy that leads to zero payoff
D The worst strategy for any scenario
A dominant strategy is one that results in the highest payoff for a player regardless of what the other players choose. In game theory, dominant strategies simplify decision-making for players in strategic games.
Which of the following is a common application of parallel algorithms?
A Binary search
B Linear regression
C Image processing
D Sorting data
Parallel algorithms are widely used in image processing tasks, where large amounts of data (pixels) need to be processed simultaneously. This improves efficiency and speed compared to sequential processing, especially for high-resolution images.
What is the time complexity of the distributed PageRank algorithm?
A O(n²)
B O(n log n)
C O(n)
D O(log n)
The distributed PageRank algorithm, used for ranking webpages, has a time complexity of O(n log n). This is because the algorithm iteratively calculates the ranks of pages based on their inbound links across distributed systems.
What is an essential component of a decision tree in machine learning?
A All data points are clustered
B All paths are random
C Nodes represent data
D No feedback loop
In decision tree algorithms, each node represents a decision based on the feature values of the data, with branches representing possible outcomes. The tree is constructed to minimize prediction error by splitting data at each decision point.
What is a key feature of unsupervised learning in machine learning?
A Finds hidden patterns in data
B Requires labeled data
C Uses reinforcement signals
D Makes predictions
Unsupervised learning involves learning patterns in data without labeled outcomes. It is used for clustering and association tasks, where the algorithm tries to identify inherent structures or groupings within the data.
What is the purpose of MapReduce in distributed systems?
A To sort data in parallel
B To improve computational complexity
C To map and reduce large datasets
D To manage memory usage
MapReduce is a framework that allows large datasets to be processed in parallel across distributed systems. The map step distributes the work, while the reduce step aggregates the results, enabling efficient data processing at scale.
What is the primary objective of the Monte Carlo method in randomized algorithms?
A To sort large datasets
B To avoid recursion
C To provide an exact solution
D To simulate random variables
The Monte Carlo method is a randomized algorithm used to estimate solutions to problems by simulating random variables. It is widely used in numerical integration, optimization, and simulating complex systems where deterministic solutions are difficult to compute.
Which of these is an example of a distributed algorithm?
A Dijkstra’s Algorithm
B Paxos algorithm
C Merge Sort
D Quick Sort
The Paxos algorithm is a distributed consensus algorithm used to achieve agreement among a group of distributed systems. It is used in scenarios where multiple machines must coordinate to ensure consistency, such as in distributed databases.
What is a key feature of parallel computing algorithms?
A Tasks are divided into independent sub-tasks
B Processors work sequentially
C Tasks are executed in isolation
D One processor handles all tasks
In parallel computing, tasks are divided into smaller sub-tasks that can be executed concurrently by multiple processors. This parallel execution allows for faster computation, especially for large-scale problems like data analysis and simulations.
Which algorithm is used in machine learning for classification?
A Prim’s Algorithm
B Dijkstra’s Algorithm
C K-Nearest Neighbors (KNN)
D Merge Sort
K-Nearest Neighbors (KNN) is a machine learning algorithm used for classification tasks. It classifies a data point based on the majority class of its nearest neighbors, making it a simple yet effective classification method.