Isaca Updated AAIA Exam Questions and Answers by yazan

False positives in fraud detection AI systems often stem from poorly optimized hyperparameters. Hyperparameters control aspects of the model’s learning process such as the learning rate, decision thresholds, and complexity penalties. When these parameters are not tuned correctly, the model can become overly sensitive and flag normal behavior as suspicious, leading to customer complaints.

“Hyperparameter tuning is essential to balance sensitivity and specificity in AI models. Improper tuning can result in a high rate of false positives or negatives, particularly in systems like fraud detection that require nuanced pattern recognition.”

Options A and B relate to data governance but do not directly cause false positives in predictions. Option C (compute scale training) may affect model efficiency, not accuracy. Thus, D is the most appropriate answer.

Generative AI tools such as large language models often have token limitations that restrict how much input they can process in a single prompt. According to the AAIA™ Study Guide, when input exceeds token limits, the model processes only the initial portion—leading to biased outputs based on early entries.

“Token limits in generative AI systems may lead to partial input processing, skewing model outputs toward the beginning of the data and introducing interpretive bias. Auditors must assess whether output reflects the full dataset.”

Options A and C describe side effects, but D identifies the most significant risk—output distortion due to truncated input.

Imputing missing values using the mean, median, or mode is a common technique to fill blanks in datasets. However, according to the AAIA™ Study Guide, this method introduces a significant risk of information distortion, especially if the data is not normally distributed or if imputed values disproportionately impact underrepresented groups.

“Simple imputation can reduce data variability and reinforce existing biases. It may also misrepresent the true distribution of the data, leading to skewed model outputs.”

Other options (B, C, D) may involve transformations, but they do not inherently cause as much bias or data misrepresentation as A.

Ensuring that AI tools are trained on properly licensed and documented data sets is critical to avoiding copyright infringement and legal exposure. The AAIA™ Study Guide emphasizes using platforms with certified and traceable training data to meet ethical and legal standards.

“Organizations must verify the provenance and licensing of data used to train AI systems. Platforms that certify data sources reduce the risk of using protected intellectual property without consent.”

Manual review (A) is resource-intensive and may not detect embedded copyright violations. Labeling (C) is not sufficient for legal protection. Suspension (D) may be excessive without first attempting remediation. Thus, B is the most strategic and effective recommendation.