1. Which of the following describes batch learning?

A) The model is trained incrementally with one instance at a time. B) The model is trained all at once on the entire dataset. C) The model requires constant retraining as new data arrives. D) The model does not retain any previous knowledge after each batch.

2. What is the goal of a classification task?

A) To categorize instances into specific classes. B) To predict a continuous numerical value. C) To identify patterns in unlabeled data. D) To train a neural network without supervision.

3. Which of the following best describes regression?

A) A method for predicting discrete class labels. B) A method for predicting a continuous target variable. C) A clustering technique. D) A method for supervised learning only.

4. What is one advantage of using a model with high generalization?

A) The model performs well on new, unseen data. B) The model memorizes training data. C) The model achieves a higher accuracy only on the training set. D) The model is slower but more accurate.

5. What is clustering in Machine Learning?

A) Grouping similar instances together based on features. B) Predicting a target value based on features. C) Categorizing data into known labels. D) Reducing the dataset to make training faster.

6. Which of the following is true for reinforcement learning?

A) The model learns by trial and error to maximize a reward. B) The model uses only labeled data. C) It’s primarily used for clustering data. D) It is a subset of unsupervised learning.

7. What is the purpose of model evaluation?

A) To assess the model's performance on unseen data. B) To visualize the data. C) To improve the training time. D) To define new features.

8. Which of these best defines supervised learning?

A) Learning from unlabeled data. B) Learning from labeled data to predict future labels. C) A system that only learns through reward feedback. D) A technique only used for numerical predictions.

9. Which term describes the process of feeding data into a machine learning model?

A) Training B) Clustering C) Evaluation D) Labeling

10. Which method is commonly used to split data for evaluating a model?

A) Training-Validation-Testing B) Visualization-Testing-Labeling C) Labeling-Training-Evaluation D) Prediction-Validation-Evaluation