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They all got help from valid, updated, and real AAIA exam dumps. The ISACA AAIA exam questions are designed and verified by experienced and qualified ISACA AAIA Exam trainers. They have verified all AAIA exam questions one by one and ensured the top standard of ISACA AAIA practice test questions.
NEW QUESTION # 121
What should be the IS auditor's GREATEST concern when an organization is mixing AI- generated content into training data without labeling?
Answer: C
Explanation:
This is known as "Data Contamination" or "Model Autophagy". When an AI model is trained on data it (or another AI) previously generated, it creates a feedback loop. Errors, hallucinations, and biases in the generated content are re-ingested as "ground truth," which causes the model to
"Collapse" and lose its ability to accurately reflect the real world. The ISACA AAIATM manual emphasizes that training data must be "Primary" and "Authentic" to ensure model integrity. Failing to distinguish between human and AI content prevents the organization from detecting and correcting systemic errors in the model's logic.
NEW QUESTION # 122
During audit planning, an IS auditor reviews the correlation matrix. Which variable pair from an electrical generation facility has the MOST significant correlation?
Answer: C
Explanation:
A correlation of -0.85 (option C) represents a strong negative correlation, indicating a meaningful inverse relationship. In AI model development, high-magnitude correlations (positive or negative) strongly influence feature selection and model behavior.
AAIA emphasizes that auditors must understand correlation strength to evaluate:
* Feature relevance
* Model weight justification
* Risk of multicollinearity
* Data quality and representativeness
The other correlations are weak or negligible.
Thus, the pair with -0.85 is the most significant.
References:
AAIA Domain 1: AI Data Relationships
AAIA Domain 2: Data Understanding and Statistical Foundations
NEW QUESTION # 123
An organization's AI ethics committee is reviewing a new hiring algorithm. Which question is MOST relevant to their review?
Answer: C
Explanation:
Ethics review in HR/hiring contexts centers on fairness and non-discrimination -- assessing whether training data or features (even indirectly, via proxies) could produce biased outcomes.
NEW QUESTION # 124
An organization is using information gathered from customer accounts to train its AI chatbot. Which of the following is the GREATEST risk associated with this practice?
Answer: A
Explanation:
The use of customer data in AI training presents a significant privacy risk, especially when the data is not properly anonymized or when consent has not been explicitly obtained. The AAIA™ Study Guide classifies the unauthorized or inadvertent disclosure of personally identifiable information (PII) as the most severe risk in such contexts.
"Training AI models on customer data without proper safeguards can result in the unintentional exposure of personal or sensitive information, leading to data breaches and compliance violations." While bias (B) and hallucinations (D) are important operational concerns, they do not pose the same regulatory and ethical severity as PII exposure. Therefore, A is the most critical risk.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Ethical and Legal Considerations in AI," Subsection: "Privacy Risks and Data Minimization"
NEW QUESTION # 125
An AI social media platform uses an algorithm to increase user engagement that could unintentionally promote divisive content. Which of the following is the BEST course of action to mitigate this risk?
Answer: D
Explanation:
The AAIATM Study Guide emphasizes that regular algorithmic audits are critical for identifying unintended consequences such as the promotion of harmful or biased content. This proactive approach helps maintain trust and ensure that algorithmic decisions align with organizational values and ethical standards.
"Auditing and monitoring AI models regularly helps detect and correct bias, drift, or other unintended behavior. It is essential for high-impact AI systems like content recommendation engines."
NEW QUESTION # 126
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