20 Sample Exam/Interview Questions and Answers for Artificial Intelligence

Here are 20 sample exam/interview questions and answers for Artificial Intelligence for bachelor studies:

1. What is the difference between artificial intelligence and machine learning?

• Artificial intelligence refers to the simulation of human intelligence in machines, while machine learning is a subset of artificial intelligence that focuses on the development of algorithms and statistical models that enable machines to improve their performance with experience.

2. What is the difference between supervised and unsupervised learning?

• Supervised learning is a type of machine learning where the algorithm is trained using labeled data, while unsupervised learning is a type of machine learning where the algorithm is trained using unlabeled data.

3. What is the difference between deep learning and traditional machine learning?

• Deep learning is a subset of machine learning that uses neural networks with multiple layers to analyze data, while traditional machine learning uses simpler algorithms to analyze data.

4. What is the difference between artificial neural networks and natural neural networks?

• Artificial neural networks are computer-based models that simulate the structure and function of natural neural networks, while natural neural networks are biological networks of neurons found in animals and humans.

5. What is the difference between a decision tree and a random forest?

• A decision tree is a type of algorithm that uses a tree-like model to make decisions, while a random forest is an ensemble of decision trees that improves the overall performance of the model by reducing overfitting.

6. What is the difference between a Bayesian network and a Markov Decision Process?

• A Bayesian network is a probabilistic graphical model that represents the dependencies between variables, while a Markov Decision Process is a model for decision-making under uncertainty that represents the states, actions, and rewards of an agent.

7. What is the difference between supervised and reinforcement learning?

• Supervised learning is a type of machine learning where the algorithm is trained using labeled data, while reinforcement learning is a type of machine learning where the algorithm learns from its own actions and experiences.

8. What is the difference between rule-based and case-based reasoning?

• Rule-based reasoning uses a set of predefined rules to make decisions, while case-based reasoning uses past experiences or cases to make decisions.

9. What is the difference between natural language processing and natural language understanding?

• Natural Language Processing (NLP) is a field of AI that deals with the interaction between computers and human languages, while natural language understanding (NLU) is a subfield of NLP that deals with the ability of computers to understand the meaning of human language.

10. What is the difference between computer vision and image processing?

• Computer vision is a field of AI that deals with the ability of computers to interpret and understand visual information from the world, while image processing is a field of computer science that deals with the manipulation and analysis of images.

11. What is the difference between a generative model and a discriminative model?

• A generative model learns the underlying probability distribution of the data, while a discriminative model learns the decision boundary between different classes.

12. What is the difference between clustering and classification?

• Clustering is the task of grouping similar data points together, while classification is the task of assigning labels to data points based on their characteristics.

13. What is the difference between supervised and semi-supervised learning?

• Supervised learning is a type of machine learning where the algorithm is trained using labeled data, while semi-supervised learning is a type of machine learning where the algorithm is trained using a combination of labeled and unlabeled data.

14. What is the difference between inductive and deductive reasoning?

• Inductive reasoning is the process of inferring general principles from specific observations, while deductive reasoning is the process of deducing specific predictions from general principles.

15. What is the difference between supervised and unsupervised learning?

• Supervised learning is a type of machine learning where the algorithm is trained using labeled data, while unsupervised learning is a type of machine learning where the algorithm is trained using unlabeled data.

16. What is the difference between decision trees and random forests?

• A decision tree is a type of algorithm that uses a tree-like model to make decisions, while a random forest is an ensemble of decision trees that improves the overall performance of the model by reducing overfitting.

17. What is the difference between genetic algorithms and simulated annealing?

• Genetic algorithms are optimization algorithms that are inspired by the process of natural selection, while simulated annealing is an optimization algorithm that is inspired by the process of annealing in metallurgy.

18. What is the difference between gradient descent and stochastic gradient descent?

• Gradient descent is an optimization algorithm that updates the parameters of a model by following the negative gradient of the loss function, while stochastic gradient descent is a variant of gradient descent that updates the parameters of a model using a random subset of the training data.

19. What is the difference between Q-learning and SARSA?

• Q-learning is a model-free, off-policy algorithm for reinforcement learning, while SARSA is a model-based, on-policy algorithm for reinforcement learning.

20. What is the difference between a generative model and a discriminative model?

• A generative model learns the underlying probability distribution of the data, while a discriminative model learns the decision boundary between different classes.

Please keep in mind that these questions and answers are just a sample and are not exhaustive. Additionally, I suggest double-checking the answers and also checking the latest Artificial Intelligence standards and best practices as the field is constantly evolving.