An IS auditor notes that an AI model achieved significantly better results on training data than on test data. Which of the following problems with the model has the IS auditor identified?
Answer(s): C
The IS auditor has identified overfitting, which occurs when an AI model performs significantly better on training data than on test data. This means the model has learned the training data too well, including noise or irrelevant patterns, resulting in poor generalization to new, unseen data.
Which of the following is MOST important to have in place when initially populating data into a data frame for an AI model?
The most important step when initially populating data into a data frame for an AI model is conducting an analysis of exploratory data that checks for incorrect data types, null values, and duplicate entries. This ensures data quality and integrity, which are foundational for effective model training and accurate results.
In order to streamline operations, a bank has deployed an AI application to automatically detect and prevent further fraud on accounts. However, customers have voiced concerns that their usual transactions are being rejected. Which of the following is the MOST likely cause of the false positives?
The most likely cause of the false positives is that the hyperparameters are not optimized. Poorly tuned hyperparameters can cause the AI model to become too sensitive, incorrectly flagging normal transactions as fraudulent. Proper optimization improves model precision and reduces false positives.
Which of the following is the PRIMARY reason IS auditors must be aware that generative AI may return different investment recommendations from the same set of data?
Answer(s): D
The primary reason generative AI may return different investment recommendations from the same data is that its computational logic is based on probabilities. Generative models often produce varied outputs due to their stochastic nature, meaning results can differ even with identical inputs.
Which of the following strategies used by modelers to enhance data accuracy has the GREATEST risk of bias and information loss?
Answer(s): B
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