1. Which of the following techniques can be used to reduce the number of features in a dataset?

A) Clustering B) Dimensionality Reduction C) Regression D) Reinforcement Learning

2. What type of Machine Learning system is most commonly used in self-driving cars?

A) Supervised Learning B) Unsupervised Learning C) Reinforcement Learning D) Semi-supervised Learning

3. Which type of learning algorithm would be used to group news articles into topics?

A) Supervised Learning B) Clustering C) Regression D) Classification

4. What is the purpose of a validation set?

A) To train the model on a different set of data B) To fine-tune model parameters before testing C) To evaluate the model after training D) To reduce overfitting

5. Which Machine Learning approach helps in making predictions on a continuous numerical target?

A) Classification B) Regression C) Clustering D) Reinforcement Learning

6. What is the main difference between batch and online learning?

A) Batch learning updates continuously, online learning does not B) Online learning updates continuously, batch learning does not C) Only batch learning uses labeled data D) Online learning can only be used with deep learning models

7. What is the key purpose of using reinforcement learning in games?

A) To classify each player's move B) To reward optimal moves and penalize poor moves C) To cluster similar strategies D) To predict the score based on previous games

8. Which of the following best describes underfitting?

A) The model performs well on training data but poorly on test data B) The model is too simple and fails to capture data patterns C) The model has memorized the training data D) The model has high accuracy on test data

9. What is one common reason for splitting data into training, validation, and test sets?

A) To improve visualization of the data B) To tune the model’s performance without overfitting to training data C) To prevent clustering issues D) None of the above

10. What is one benefit of reducing the dimensionality of a dataset?

A) It increases the dataset size B) It can improve model performance by reducing noise C) It allows the use of unsupervised learning D) It only applies to clustering problems