1. Which type of Machine Learning algorithm is most appropriate when the goal is to detect patterns without labels?

A) Supervised Learning B) Unsupervised Learning C) Reinforcement Learning D) None of the above

2. What is an example of online learning in Machine Learning?

A) A model that retrains only after a significant amount of new data is collected B) A model that updates itself continuously as new data comes in C) A model that is periodically evaluated on a validation set D) A model that operates only in a batch processing mode

3. Which method would you use to categorize email messages into spam or not spam?

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

4. In reinforcement learning, what is typically used to guide the learning process?

A) Data labels B) Rewards and penalties C) Supervised learning feedback D) Model accuracy

5. What does overfitting mean in the context of Machine Learning?

A) The model generalizes well to new data B) The model performs well on training data but poorly on test data C) The model has low complexity D) The model has not yet learned enough patterns

6. What is one advantage of batch learning over online learning?

A) Faster adaptation to new data B) Requires less computing resources when data is sparse C) Allows the model to learn at any time from small data updates D) None of the above

7. What does it mean when a dataset is "labeled"?

A) Each data instance has an associated output or tag B) The dataset is divided into clusters C) The dataset does not contain any missing values D) The dataset only contains numbers

8. Which Machine Learning approach aims to discover structure in data without labels?

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

9. What is the purpose of cross-validation in model evaluation?

A) To visualize data distributions B) To improve model generalization by testing on multiple data subsets C) To create labels for the dataset D) None of the above

10. What is dimensionality reduction?

A) Increasing the size of the dataset B) Reducing the number of features in a dataset while retaining important information C) Clustering similar instances together D) None of the above