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Question 372 (1 point) (=| @) Listen | Which factor could lead to hallucinations in Al by causing the model to capture noise or specific patterns that do not generalize well to new, unseen data? Select one. C) Adversarial attacks ) Lack of diversity in training data o ) Overfitting a C) Data bias and imbalance Question 33 (1 point) [BE] 4) Listen | | In image classification, what could lead to a well-trained model misclassifying a stop sign as a yield sign? Select one. C) Adversarial attacks O Data anomalies and outliers O Inadequate model interpretability C) Lack of diversity in training data Question 34 (1 point) (=| @) Listen What might contribute to hallucinations in Al by making it challenging to understand the decision-making process due to poor transparency? Select one. C) Inadequate model interpretability ) Data anomalies and outliers a ) Overfitting io O Data bias and imbalance Question 35 (1 point) (=| @) Listen | Which scenario could lead to hallucinations in an anomaly detection model, causing the Al to misinterpret a rare but valid transaction as fraudulent? Select one. C) Overfitting ) Adversarial attacks C) Data anomalies and outliers C) Lack of diversity in training data Question 36 (1 point) (=| @) Listen | What is a simple initial step you can take to begin to recognize hallucinations and inefficiencies in Al generated content? Select one. C) Trust the output from your favorite generative Al tool. ) Refuse to compare information to other reliable sources. io - ) Attend an expensive webinar on generative Al training. Cc C) ultivate a habit of questioning information and outputs