1. Which of the following is not a standardized data-mining process model?, A) CRISP-DM, B) SEMMA, D)Naïve Bayes, 2. What does the “M” in CRISP-DM stand for?, A) Mining, B) Management , C) Modelling , 3. What is the main goal of the classification method in data mining?, A) To discover hidden associations among products, B) To predict categorical outcomes based on historical labeled data, C) To visualize data distributions, 4. What is the main goal of Business Understanding in CRISP-DM?, A) Collect and clean data, B) Define project objectives and convert them to a data-mining problem, C) Deploy the model, 5. The SEMMA process was developed by:, A) IBM, B) KDnuggets, C) SAS Institute, 6. In the KDD process, data mining is:, A) A single step within a larger discovery process, B) The entire workflow, C) Pre-processing, 7. Classification in data mining belongs to which learning type?, A) Unsupervised, B) Supervised, C) Semi-supervised, 8. Which of the following is a key difference between classification and regression?, A) Classification predicts continuous values, regression predicts discrete ones, B) Both predict continuous values, C) Classification predicts categorical labels, regression predicts numeric values, 9. Which statement best distinguishes classification from clustering?, A) Classification uses labeled data, B) Clustering uses labeled data, C) Both use labeled data, 10. Which metric measures how well a model predicts true outcomes?, A) Precision, B) AUC, C) Predictive Accuracy, 11. The ability of a model to remain effective with noisy data is called:, A) Robustness, B) Interpretability, 12. In Decision Tree construction, which step comes first?, A) Pruning unnecessary branches, B) Selecting the splitting attribute for the root node, C) Assigning test data to leaves, 13. During k-fold cross-validation, each fold:, A) Is tested twice, B) Is used for training only, C) Is used for testing once, 14. An AUC value of 0.5 represents:, A) Perfect classification, B) Random chance, C) Severe overfitting, 15. What is the role of the Recurse and Terminate step in Decision Tree construction?, A) It splits nodes based on entropy, B) It stops growing the tree when specific conditions are met, C) It measures accuracy.

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