Pass the Isaca Advanced in AI Audit AAIA Questions and answers with CertsForce

The effectiveness of an AI-driven business process—such as categorizing customers for promotional campaigns—depends on how well it supports defined business objectives. The AAIA™ Study Guide recommends validating that AI methodology aligns with intended outcomes as part of performance auditing.

“Effectiveness is best measured by assessing whether the AI logic contributes meaningfully to business goals. Output alignment with organizational KPIs or campaign strategies provides clear evidence of functional success.”

Options A and D support operational resilience and quality assurance. Option C is a privacy technique, not directly tied to effectiveness validation. Thus, B is correct.

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.

TheGREATEST concernis when the AI system’sdecision-making process is unexplainable(A), especially for high-impact or regulated decisions. AAIA stresses that explainability is essential for accountability, fairness assessments, compliance, and public trust. If decisions cannot be explained, the organization cannot validate fairness, detect bias, or justify outcomes to regulators or affected individuals.

Cloud hosting (B) is manageable through standard controls. Retraining frequency (C) affects performance but not core ethics. Draft documentation (D) is a procedural issue, not an ethical barrier. Unexplainable decision logic is thefoundational ethical risk.

The GREATEST concern isinsufficient access controls(D), which can lead tounauthorized exposure of customer data—a severe privacy, security, regulatory, and reputational risk. Chat logs often contain personally identifiable information and sensitive communications. AAIA prioritizesdata confidentiality, access control, and privacy obligationsas highest-risk elements, particularly for customer-interactive AI systems.

Inaccurate chatbot responses (C) affect reputation but are less severe than data breaches. Obsolete procedures (B) matter but pose less immediate harm. Limited capability to incorporate data (A) affects performance but not critical risk.