Artificial Intelligence (AI) Multiple Choice Questions

July 17, 2024July 17, 2024 Pradeep Maurya

Are you ready to test your knowledge of artificial intelligence (AI)? This set of 50 multiple-choice questions covers a variety of AI topics, including machine learning, neural networks, natural language processing, and more. Whether you’re a student, a professional, or just interested in AI, these questions will help you review AI concepts and discover new ones.

Test Your Knowledge with 50 Artificial Intelligence (AI) Multiple-Choice Questions

Each question has four possibilities, and the correct answer is supplied so you can double-check your responses.

 

Results

#1. What is Artificial Intelligence ………………………..?

The study of computer algorithms that improve automatically through experience

The branch of computer science concerned with making computers behave like humans

A subset of machine learning focused on neural networks

A computer system designed to simulate human conversation

#2. Which of the following is not an application of AI?

Speech recognition

Expert systems

Email spam filtering

Database management systems

#3. Which technique is used in AI for making machines learn from past experiences?

Machine Learning

Data Mining

Information Retrieval

Data Analytics

#4. Who is known as the father of Artificial Intelligence?

Alan Turing

John McCarthy

Marvin Minsky

Andrew Ng

#5. What is a neural network?

A system of hardware and/or software patterned after the operation of neurons in the human brain

A network of computers designed to share data

A type of data storage technique

A branch of AI focused on rule-based systems

#6. What does NLP stand for in the context of AI?

Natural Learning Processing

Neural Learning Protocol

Natural Language Processing

National Language Program

#7. Which of the following is a type of reinforcement learning?

Supervised Learning

Unsupervised Learning

Semi-Supervised Learning

Q-Learning

#8. Which programming language is most commonly used for AI and Machine Learning?

Java

Python

C++

JavaScript

#9. In which type of AI system do the machines have the ability to perform tasks without human intervention?

Weak AI

Narrow AI

General AI

Strong AI

#10. What is the main goal of AI?

To create systems that think exactly like humans do

To make smart systems that behave intelligently

To automate all human jobs

To improve the performance of computers

#11. Which of the following is an AI technique for solving problems by mimicking the process of natural evolution?

Neural Networks

Genetic Algorithms

Fuzzy Logic

Decision Trees

#12. Which component of AI deals with the ability of a system to adjust its behavior based on the results of previous actions?

Learning

Perception

Reasoning

Language Understanding

#13. Which of the following is a goal of machine learning?

To create algorithms that can learn from and make predictions on data

To develop systems that can only process structured data

To design hardware systems that mimic the human brain

To develop programming languages specifically for AI

#14. Which AI approach is based on the idea that intelligent behavior can be achieved by manipulating symbols and rules?

Connectionism

Symbolic AI

Evolutionary Computing

Sub-symbolic AI

#15. What does the Turing Test evaluate?

The ability of a machine to understand and generate human language

The efficiency of machine learning algorithms

The ability of a machine to show intelligent behaviour that looks like a human

The performance of AI systems in real-world tasks

#16. Which of the following is not a type of machine learning?

Supervised Learning

Unsupervised Learning

Reinforcement Learning

Augmented Learning

#17. Which algorithm is used for classification and regression in machine learning?

K-Means

Decision Trees

Apriori

PageRank

#18. What is overfitting in the context of machine learning?

When a model performs well on training data but poorly on unseen data

When a model performs well on both training and unseen data

When a model performs poorly on both training and unseen data

When a model performs well only on unseen data

#19. Which of the following is a measure of how well a model generalizes to new data?

Accuracy

Precision

Recall

Validation

#20. What is the purpose of cross-validation in machine learning?

To select the best model for the data

To improve the model’s performance on the training data

To estimate the performance of a model on new data

To reduce the complexity of the model

#21. Which of the following is an example of a generative model in AI?

Support Vector Machines

Linear Regression

Gaussian Mixture Models

K-Nearest Neighbors

#22. What is the main difference between supervised and unsupervised learning?

Supervised learning requires labeled data, while unsupervised learning does not

Supervised learning is used for classification tasks, while unsupervised learning is used for regression tasks

Supervised learning uses neural networks, while unsupervised learning uses decision trees

Supervised learning is more complex than unsupervised learning

#23. Which AI technique is based on the concept of reward and punishment?

Supervised Learning

Unsupervised Learning

Reinforcement Learning

Transfer Learning

#24. What is the purpose of a loss function in machine learning?

To measure how well the model fits the training data

To optimize the model’s parameters

To preprocess the data before training

To visualize the model’s performance

#25. Which of the following is a popular library for machine learning in Python?

NumPy

Matplotlib

TensorFlow

Pandas

#26. Which of the following is not a common activation function used in neural networks?

Sigmoid

ReLU

Tanh

Entropy

#27. What is the purpose of an optimizer in training a neural network?

To reduce the size of the dataset

To minimize the loss function

To preprocess the input data

To visualize the training process

#28. Which of the following is an unsupervised learning algorithm?

Linear Regression

Decision Trees

K-Means Clustering

Logistic Regression

#29. Which term refers to the ability of an AI system to explain its reasoning and decisions?

Interpretability

Scalability

Efficiency

Robustness

#30. What is the main advantage of deep learning over traditional machine learning algorithms?

Deep learning requires less data

Deep learning can automatically learn features from raw data

Deep learning models are simpler to interpret

Deep learning is always more accurate

#31. Which of the following is a popular framework for deep learning?

Scikit-Learn

TensorFlow

NLTK

OpenCV

#32. What is transfer learning in the context of deep learning?

Training a model on a large dataset and then fine-tuning it on a smaller, related dataset

Transferring data from one model to another

Using multiple models to improve performance

Training a model on data from different domains

#33. Which of the following is a common method for preventing overfitting in machine learning models?

Increasing the model complexity

Reducing the amount of training data

Using regularization techniques

Ignoring validation data

#34. What is the purpose of a confusion matrix in evaluating classification models?

To measure the model’s accuracy

To visualize the performance of a classification algorithm

To compute the loss function

To preprocess the input data

#35. Which of the following is a type of recurrent neural network (RNN)?

Convolutional Neural Network (CNN)

Long Short-Term Memory (LSTM)

Generative Adversarial Network (GAN)

Autoencoder

#36. What is the main application of convolutional neural networks (CNNs)?

Natural language processing

Image and video recognition

Time series forecasting

Reinforcement learning

#37. Which AI technique involves training two neural networks to compete with each other?

Transfer Learning

Reinforcement Learning

Generative Adversarial Networks (GANs)

Support Vector Machines (SVMs)

#38. What is the purpose of an embedding layer in neural networks?

To preprocess the input data

To convert categorical data into numerical format

To reduce the dimensionality of the input data

To extract features from raw data

#39. Which of the following is an example of a pre-trained language model?

VGG16

ResNet

BERT

AlexNet

#40. Which of the following is not a common evaluation metric for classification models?

Accuracy

Precision

Recall

Mean Squared Error

#41. What is the main purpose of feature scaling in machine learning?

To increase the accuracy of the model

To reduce the complexity of the model

To normalize the range of the features

To visualize the data

#42. Which of the following is a technique used for dimensionality reduction?

Principal Component Analysis (PCA)

K-Means Clustering

Decision Trees

Random Forest

#43. Which AI subfield focuses on enabling machines to understand and generate human language?

Computer Vision

Robotics

Natural Language Processing (NLP)

Expert Systems

#44. What is the purpose of backpropagation in training neural networks?

To update the weights of the network based on the error

To preprocess the input data

To initialize the weights of the network

To reduce the size of the network

#45. Which of the following is an ensemble learning method?

Linear Regression

Random Forest

Support Vector Machines

K-Nearest Neighbors

#46. What is the main difference between a shallow and a deep neural network?

The number of input features

The number of output classes

The number of hidden layers

The size of the training data

#47. Which of the following is a common method for optimizing hyper parameters in machine learning models?

Cross-validation

Gradient Descent

Grid Search

Regularization

#48. Which AI technique is used to find the shortest path in a graph?

Decision Trees

Support Vector Machines

A* Algorithm

K-Means Clustering

#49. What is the main goal of unsupervised learning?

To classify data into predefined categories

To make predictions based on labeled data

To find hidden patterns and structures in the data

To reduce the size of the dataset

#50. Which of the following is a common application of AI in healthcare?

Social media analysis

Fraud detection

Predictive diagnostics

Supply chain optimization

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Pradeep Maurya

Pradeep Maurya is the Professional Web Developer & Designer and the Founder of  “Tutorials website”. He lives in Delhi and loves to be a self-dependent person. As an owner, he is trying his best to improve this platform day by day. His passion, dedication and quick decision making ability to stand apart from others. He’s an avid blogger and writes on the publications like Dzone, e27.co

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