AI0-001 AI Concepts and Foundations Practice Questions

Explanation: Option C (Overfitting) is correct because the model learned patterns specific to the historical data from the original factory, including noise and factory-specific nuances, rather than generalizable features. When applied to new data from a different factory, those learned patterns do not hold, causing poor performance. This is the classic symptom of overfitting: high accuracy on training data but low accuracy on unseen data.

Explanation: High bias indicates the model is underfitting the data, meaning it is too simple to capture the underlying patterns. Adding polynomial features increases model complexity by introducing non-linear terms, which allows the linear regression model to better fit the training data and thus reduce bias.

Explanation: The high training accuracy (99%) and lower validation accuracy (85%) indicate overfitting, where the model memorizes training data but fails to generalize. Dropout layers randomly deactivate neurons during training, forcing the network to learn more robust features and reducing overfitting. This technique directly addresses the discrepancy by improving validation performance without sacrificing training capacity.

Explanation: Machine learning (ML) enables a chatbot to learn from new data and adapt to unseen queries, unlike a static rule-based system. By training on historical conversations, an ML model can generalize patterns and handle novel inputs without requiring explicit rules for every scenario.

Explanation: High precision means that when the model flags a transaction as fraudulent, it is very likely correct. However, low recall indicates that the model misses a significant proportion of actual fraudulent transactions. Therefore, the most likely business impact is that many fraudulent transactions go undetected, leading to financial losses.