##### Scenario Science-breadth interview for an Amazon Applied Scientist role, assessing foundational understanding of common machine-learning algorithms. ##### Question a) Explain in detail how a decision-tree model is trained: how split points are chosen, how stopping criteria/pruning work, and how overfitting is avoided. b) Name at least three clustering algorithms and describe the core principle behind each one. ##### Hints Cover impurity measures (Gini/entropy), information gain, pre-pruning vs post-pruning; for clustering mention K-Means, DBSCAN, Hierarchical, Gaussian Mixture, etc., and highlight distance, density or probabilistic assumptions.