AAIA Exam Dumps - Isaca Advanced in AI Audit Questions and Answers

Exploratory Data Analysis (EDA) is critical during the initial stages of AI model development. According to the AAIA™ Study Guide, performing EDA—including identifying null values, incorrect data types, or duplicates—ensures that the data fed into the model is clean and reliable.

“Initial data frames should be subject to thorough EDA to uncover data quality issues. These issues, if not addressed early, can severely affect model training and predictive accuracy.”

While separating data sets (B) and visualizations (A) are important steps in later phases, C is foundational to ensure readiness for model training. Risk assessments (D) are necessary but not the first operational step.

The ethical use of customer data is rooted in respecting privacy, maintaining informed consent, and enabling data subjects to exercise control over their personal information. The AAIA™ Study Guide clearly outlines that obtaining explicit consent and providing opt-out capabilities align with principles of data protection and ethical AI.

“Ethical AI implementation includes transparency in data collection, clear consent mechanisms, and the right of users to opt out or control their personal data usage. Retail and consumer applications must ensure that personalized services do not override these data subject rights.”

Options B and D violate principles of minimal data use and consent, while C may create unnecessary privacy exposure. Therefore, A is the correct and most ethical practice.

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.

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.

Auditors using AI tools to generate reports or updates must ensure the output is reliable, factually accurate, and complete. As per the AAIA™ Study Guide, accountability for audit content remains with the auditor, even when generative AI assists with content creation. Therefore, verifying the completeness and correctness of AI-generated content is the primary responsibility.

“AI-generated audit outputs must be validated against source data and professional standards. The auditor must critically assess AI contributions and not rely on them without human oversight.”

Options A and C focus on output consistency, not accuracy. Option D is part of the communication phase, not validation. Therefore, B is the auditor's core duty.

Precision measures the proportion of true positives among all positive predictions. A low precision rate indicates a high rate of false positives. The AAIA™ Study Guide recommends using precision when the goal is to minimize incorrect positive alerts, which is especially relevant in fraud detection, cybersecurity, and classification models.

“Precision is the key metric when false positives have a significant operational cost. It provides insight into the model’s ability to avoid incorrect positive classifications.”

Accuracy and recall give broader insights, but only precision directly measures false positive risk. Completeness is not a standard ML metric.

An AI usage policy sets the foundation for safe, ethical, and effective AI deployment. According to the AAIA™ Study Guide, having an AI policy in place ensures that users understand acceptable behaviors, limitations, and responsibilities when interacting with AI tools.

“AI acceptable use policies promote governance by clearly outlining the dos and don’ts of AI interaction, preventing misuse and aligning user activity with organizational values and compliance standards.”

Other actions (B, C, D) are important in operations and risk management but should follow the establishment of governance protocols through a usage policy. Hence, A is the highest-priority prerequisite.

Only reviewing an ML model’s performance one time prior to migrating to production (option C) is of greatest concern. The AAIA™ Study Guide emphasizes that “AI and ML models require continuous monitoring and periodic performance reviews in production to detect issues such as data drift, model degradation, or evolving risk factors.” A single pre-production review fails to capture these changes and risks, potentially resulting in undetected failures or compliance issues.

Periodic (including annual) and event-driven reviews are necessary to ensure ongoing model reliability.

LIME (Local Interpretable Model-Agnostic Explanations) is a leading tool for explaining individual AI predictions by approximating the behavior of complex models with simple, interpretable ones in localized regions. The AAIA™ Study Guide highlights LIME as highly effective for providing transparency and interpretability to non-technical stakeholders.

“LIME enables auditors to demonstrate how specific input features influenced an AI decision, facilitating trust and stakeholder understanding—especially in regulated or high-impact contexts.”

Options A, B, and C are technical modeling techniques but do not prioritize stakeholder-friendly explanation. Therefore, D is best for transparency.

When an AI system begins givingincorrect financial advice, the FIRST step is tosuspend the chatbot(D) to prevent ongoing harm. AAIA emphasizes protecting users from unsafe decisions as the top priority in AI operations.

Suspension allows the organization to:

Stop distribution of harmful advice

Assess impact and identify faulty API dependencies

Prevent regulatory or customer harm

Conduct root-cause analysis safely

Retraining (C) is corrective but must occurafterassessment. Adding rules (B) risks masking deeper issues. Speed patches (A) are irrelevant to correctness.