1. What is a key advantage of unsupervised learning?

A) It does not require labeled data B) It is used exclusively for classification tasks C) It can provide direct feedback to improve the model D) It is only used for reinforcement learning

2. What is a characteristic of supervised learning?

A) It can work with both labeled and unlabeled data B) It requires labeled data to train the model C) It is exclusively used in clustering tasks D) It is not affected by the data labels

3. Which learning method is often used to recommend products based on user preferences?

A) Reinforcement Learning B) Unsupervised Learning C) Supervised Learning D) Clustering

4. In online learning, how is the model updated?

A) The model is retrained from scratch every time B) The model is updated incrementally as new data arrives C) The model never updates once trained D) The model is updated only when accuracy drops

5. What type of task would benefit from reinforcement learning?

A) Predicting house prices B) Image classification C) Training an agent to play a game D) Recommending music to users

6. Why is model evaluation essential before deployment?

A) To ensure the model performs well on unseen data B) To make sure it has high accuracy on training data only C) To determine if it has learned the training data well D) To check if it has minimized the model's training time

7. What type of dataset would be used in supervised learning?

A) Labeled data B) Unlabeled data C) Data without any missing values D) Data used for clustering

8. Which of the following statements best describes a regression task?

A) It predicts categorical class labels B) It predicts continuous numerical values C) It is used only in clustering D) It only works with labeled data

9. Which of the following tasks would benefit from dimensionality reduction?

A) Predicting movie ratings B) Analyzing high-dimensional gene expression data C) Clustering based on few characteristics D) Labeling images

10. Why might a model experience overfitting?

A) It learns patterns specific to the training data, not generalizable to new data B) It has been trained on too many features and examples C) It has learned general patterns that apply to all data D) It lacks sufficient data for training