AAIA Exam Dumps - ISACA Advanced in AI Audit (AAIA)

Generative AI systems, particularly those based on transformer models, produce outputs using probabilistic computations. As a result, even when given the same input data, these models may generate different outputs depending on sampling strategies (e.g., temperature, top-k sampling).

“Generative AI operates probabilistically, meaning that outputs can vary with each run based on stochastic sampling techniques. This variability is expected and must be accounted for in risk-sensitive environments like finance.”

While A and B refer to limitations and architecture, and D is unrelated to logic, C directly explains the output inconsistency.

Overfitting occurs when a model performs very well on training data but poorly on unseen data, indicating that the model has learned patterns specific to the training set rather than generalizing effectively. The AAIAâ„¢ Study Guide identifies overfitting as a common problem that impacts model reliability.

“Overfitting limits the model's applicability to real-world scenarios. It reflects excessive tailoring to the training data and poor performance on new, diverse inputs.”

Underfitting (A) would result in poor performance on both training and test data. Generalization (C) is the desired state, and bias (D) is a separate issue. Therefore, B is correct.

Accountability for data management (option D) is the most crucial consideration. The AAIA™ Study Guide emphasizes that “clear roles, responsibilities, and ownership for data management activities are central to trustworthy AI systems, as they ensure compliance, traceability, and the consistent application of policies and controls.”

Retention, portability, and data training practices are important, but accountability is foundational for the enforcement and monitoring of all other governance practices.

An AI model inventory documents the models in use, their purposes, and how they support specific business functions. According to the AAIAâ„¢ Study Guide, maintaining a comprehensive AI model inventory allows auditors to trace model objectives, performance metrics, and use cases back to business goals.

“A well-maintained AI model inventory supports governance and alignment by offering a centralized view of model functions, business integration, and ownership. It ensures transparency and strategic coherence.”

While policies and assessments are important, only the inventory directly shows which AI models exist and their connection to organizational objectives.

Inconsistent data formatting means the AI model is receiving inputs that do not match what it was trained to understand. The best corrective action is todocument and enforce technical specificationsfor incoming data (option A).

AAIA highlights this as a fundamental data governance requirement:

Field formats

Data types

Encoding rules

Required attributes

Normalization methods

Without strict specifications, the model cannot reliably parse or classify inputs.

Option B does not address the root cause.

Option C improves human response but not data consistency.

Increasing model complexity (D) adds risk and does not fix inconsistent formatting.

The primary advantage ofK-fold cross validationis that it uses multiple train/test splits, cycling through all folds so that each observation is used both for training and testing at different points. This process provides a more reliable estimate of model performance andreduces the risk of overfitting to a single split(option D). It is an established best practice in model evaluation and aligns with AAIA’s emphasis ontesting techniques for AI solutions and data analytics.

Option A is not specific to regressions; cross validation can be used for classification and other models as well. Option B can actually increase computational cost since multiple models are trained. Option C misunderstands bias–variance trade-offs; increasing K doesn’t simply “reduce model bias.” The key advantage remains the use of repeated, varied splits to better assess generalization and guard against overfitting.