The AAIA™ Study Guide highlights that generative AI tools can significantly enhance audit productivity, especially in content generation tasks. Drafting executive summaries-condensing complex findings into clear, concise formats-is a high-impact use case that benefits from generative AI's natural language generation capabilities.
"Generative AI can be leveraged by auditors to automate reporting sections, such as executive summaries, ensuring consistency and saving time without compromising content clarity." While A, C, and D are valid uses, B delivers the most value by expediting a time-intensive and high-level reporting task in complex audits.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI in Audit Processes," Subsection: "Generative AI Applications in Audit Reporting"

Inventing nonexistent medical test results is a form ofhallucinationcommonly associated with generative AI models.Reducing top-p sampling(A) restricts the model to choosing from the most probable next tokens, reducing randomness and preventing the generation of fabricated or implausible content. AAIA emphasizes configuration tuning (temperature, top-k, top-p) as critical controls for mitigating hallucinations.
Increasing context (B) helps provide more information but does not directly reduce hallucinations. Increasing temperature (C) would make hallucinationsworseby adding randomness. Frequency penalties (D) discourage repetition, not hallucinations. The strongest mitigation isreducing sampling randomnessvia reduced top-p.
References:
ISACA,AAIA Exam Content Outline- Domain 2: AI Operations (generative AI safeguards, hallucination mitigation).

Incomplete training dataoften leads to underrepresentation of certain applicant types, products, or scenarios.
In credit and lending, this typically translates intosystematic bias: some groups are evaluated on richer historical patterns, while others are evaluated on sparse or unrepresentative information. The greatest associated risk is thereforeunfair loan decisions(A), which can manifest as unjustified rejections, inappropriate pricing, or inconsistent risk assessments.
While delays (B), reduced satisfaction (C), or increased manual work (D) may occur, they are secondary operational issues. AAIA highlights that for financial services, the central risks includefairness, discrimination, regulatory compliance, and reputational impact. Incomplete data directly undermines fairness and can violate lending regulations and internal risk appetite.
References:
ISACA,AAIA Exam Content Outline- Domain 1: AI Governance and Risk (risk categories, including fairness and discriminatory outcomes).
ISACA AI ethics content on data completeness and representativeness in decisioning systems.

The AAIA™ Study Guide defines the core purpose of machine learning as the ability to enable systems to learn from data and make decisions or perform tasks that typically require human cognitive functions. ML allows AI systems to identify patterns, learn from historical data, and automate complex decision-making.
"Machine learning empowers systems to simulate aspects of human intelligence, including pattern recognition, language understanding, and decision-making. It forms the backbone of many AI applications designed to replace or augment human tasks." While visual analysis (A) and statistical inference (D) are functions of ML, they are subsets-not primary goals. Explainability (B) is important but is not a core ML function. Thus, C best represents the primary objective.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Fundamentals and Technologies," Subsection: "Machine Learning Basics and Objectives"

Documenting model updates and retraining sessions to ensure traceability (option C) is the hallmark of mature, audit-ready change management in AI. The AAIA™ Study Guide states, "Traceability and documentation of all changes-including retraining events, parameter adjustments, and update rationales- enable effective audit trails, accountability, and regulatory compliance for AI systems." While reviews and comparisons are useful, only comprehensive documentation guarantees ongoing transparency and effective management.
Reference:ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Change Management and Traceability in AI"