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NEW QUESTION # 112
An organization is using a large language model (LLM) to assist in evaluating loan applications, but the training data used is known to be incomplete. Which of the following is the GREATEST associated risk?
Answer: D
Explanation:
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.
NEW QUESTION # 113
Which of the following is the PRIMARY purpose of an AI acceptable use policy?
Answer: C
NEW QUESTION # 114
An IS auditor is reviewing change management documentation of an AI model. Which of the following would pose the GREATEST risk to the model?
Answer: C
Explanation:
In AI development, a " seed " ensures that random processes (like weight initialization) are reproducible. If an A/B test compares two models using different seeds, the auditor cannot tell if the performance difference is due to the model changes or simply due to " random luck " in how the weights were initialized. This invalidates the test results. For a fair " apple-to-apples " comparison, the seed should remain consistent.
Tuning on a training set (Option B) is standard, though it risks overfitting; however, the lack of scientific control in testing (Option C) is a more immediate risk to the integrity of the change management process.
NEW QUESTION # 115
A retail organization uses an AI model to forecast inventory based on customer purchasing trends and updates the model quarterly. The model recently failed to recognize a surge in demand during a popular shopping season. Which of the following issues does this situation BEST demonstrate?
Answer: A
Explanation:
Data drift occurs when the statistical properties of input data change over time, impacting the accuracy of an AI model. In this case, the model failed to adapt to recent demand trends, indicating that the data on which it was trained no longer reflects current behavior.
"Data drift leads to performance degradation in AI systems when real-world input data shifts away from training data patterns. Monitoring for drift is essential, particularly in dynamic environments such as retail." Although training diversity (A) and outlier detection (D) are relevant to accuracy, only B addresses the temporal misalignment between training data and real-time data. Overfitting (C) would more likely cause poor generalization in other contexts.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Operations and Performance," Subsection: "Model Monitoring and Data Drift Detection"
NEW QUESTION # 116
An IS auditor finds that an AI model's outputs are not being reviewed. Which of the following would BEST address this risk?
Answer: A
Explanation:
WhenAI outputs are not being reviewed, the primary concern is thatincorrect, biased, or non-compliant decisionsmay go undetected. The best way to address this risk is to implement avalidation process for AI decisions(B), which may include human-in-the-loop checks, sampling-based reviews, escalation criteria, and formal approval workflows. AAIA emphasizes the importance ofsupervision and validation of AI outputs, particularly where decisions have financial, legal, or safety implications.
A larger training dataset (A) does not guarantee correctness or fairness and does not replace oversight.
Regular retraining (C) can be beneficial but can also propagate errors if not validated. Prompt templates (D) are relevant mainly for generative and prompt-based systems, and they do not constitute systematic review of outputs. Therefore, astructured validation processis the most direct and effective control.
References:
ISACA,AAIA Exam Content Outline- Domain 2: AI Operations (Supervision of AI Solutions).
ISACA audit guidance on AI decision validation and human oversight.
NEW QUESTION # 117
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