AI Concepts for IT Specialists

What does "AI" stand for in the context of computer science?

• Automated Integration

• Artificial Intelligence

• Automated Instruction

• Artificial Interaction

Which of the following best describes a "neural network" in AI?

• A physical network of computers connected together

• A type of database used to store large amounts of data

• A computational model inspired by the structure of the human brain

• A programming language used to write AI software

What is "machine learning"?

• Teaching machines to physically move on their own

• A subset of AI where systems learn and improve from experience without being explicitly programmed

• A method of manually programming every decision a computer makes

• The process of building computer hardware

Which of the following is an example of supervised learning?

• A robot exploring a new environment without any guidance

• A program grouping similar news articles together without labels

• A spam filter trained on labeled emails marked as "spam" or "not spam"

• An AI playing a game by trial and error

What is the term for the large collection of data used to train an AI model?

• Algorithm

• Dataset

• Parameter

• Output layer

Which of the following is an example of a real-world application of Natural Language Processing (NLP)?

• A self-driving car detecting road signs

• A virtual assistant like Siri understanding and responding to voice commands

• A robot arm assembling car parts in a factory

• A computer program solving mathematical equations

What is "overfitting" in the context of machine learning?

• When a model performs well on both training and new data

• When a model is too simple to capture patterns in the data

• When a model learns the training data too well, including its noise, and performs poorly on new data

• When a model is trained using too little data

A student trains an AI model to recognize cats and dogs. The model achieves 99% accuracy on training data but only 55% accuracy on new test data. What is the most likely cause of this problem?

• The model was not trained long enough

• The model is overfitting to the training data

• The model does not have enough layers

• The training data contained too many images

Which of the following best explains the difference between supervised and unsupervised learning?

• Supervised learning uses computers, while unsupervised learning uses humans

• Supervised learning uses labeled data to train models, while unsupervised learning finds patterns in unlabeled data

• Supervised learning is faster than unsupervised learning

• Unsupervised learning always produces more accurate results than supervised learning

In a decision tree, what does each "branch" represent?

• A final prediction or output

• A possible outcome or decision based on a condition

• A layer in a neural network

• A type of training data