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NEW QUESTION # 54
Which of the following controls helps mitigate the risk of competitors poisoning data utilized by a machine learning (ML) model performing sentiment analysis of product reviews?
Answer: C
Explanation:
To prevent data poisoning, especially in systems relying on publicly submitted content such as product reviews, access authentication is critical. The AAIATM Study Guide specifies that authenticated input sources help ensure data integrity and traceability, reducing the likelihood of adversarial or malicious contributions.
"Limiting review input to authenticated users restricts unauthorized actors--such as competitors or bots--from submitting biased or harmful data. This control protects model training and outputs from being manipulated."
NEW QUESTION # 55
An organization has introduced an AI chat system where customers can enter their preferences and the system returns the best product selections. Which of the following is the BEST way to mitigate the risk of the system providing suggestions that may upset customers?
Answer: A
Explanation:
The risk described is thatcustomer-facing suggestionsmay be inappropriate, insensitive, or offensive. The BEST mitigation is toperform testing of diverse scenarios(B)-including edge cases, demographic variations, and sensitive contexts-to confirm that outputs remain within acceptable business, ethical, and customer-experience thresholds. AAIA highlightsscenario-based testingandfairness/impact assessmentsas key practices, especially where recommendations directly influence customer interactions.
Increasing data volume (A) does not ensure fairness or sensitivity. Monitoring servers (C) focuses on technical health, not content appropriateness. Threat analysis (D) is important for security but does not directly address emotional or ethical impacts of model outputs. Therefore, structured,diverse scenario testing is the most targeted and effective approach.
References:
ISACA,AAIA Exam Content Outline- Domain 2 & Domain 5: Testing techniques; ethical and user-impact considerations.
ISACA AI guidance on scenario testing for fairness, appropriateness, and user impact.
NEW QUESTION # 56
The GREATEST benefit of using AI auditing techniques over traditional methods is that AI auditing techniques can:
Answer: D
Explanation:
AI auditing techniques excel at identifying complex data patterns (option C), which is their primary advantage over manual or traditional audit approaches. The AAIATM Study Guide states, "AI- based audit tools can process massive volumes of data at speed and depth, detecting anomalies, trends, or relationships that might be invisible to human auditors or unfeasible to uncover manually." AI does not fully eliminate the need for human involvement, nor does it guarantee compliance or the elimination of bias, but it can analyze intricate patterns in large, multidimensional data sets.
NEW QUESTION # 57
An IS auditor is looking to expedite reporting for an audit with complex issues. Which of the following would be the MOST effective way for the auditor to use generative AI?
Answer: C
Explanation:
The AAIATM 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."
NEW QUESTION # 58
Which of the following is the MOST important reason to establish AI governance structures that extend beyond regulatory compliance?
Answer: C
Explanation:
While regulatory compliance is essential, AAIA underlines thatethical integritymust guide AI design, deployment, and monitoring. Regulations often lag behind technological capabilities; thus, relying solely on compliance leaves gaps in areas such as fairness, transparency, human dignity, and societal impact. The MOST important reason to extend governance structures beyond compliance is toensure ethical integrity throughout the AI life cycle(C) - from data collection and model design to deployment, monitoring, and retirement.
Option A focuses on privacy only, a subset of broader ethical considerations. Option B is valid but secondary; reputational protection is often a consequence of doing the right thing ethically. Option D (guardrails) is part of governance, but the overarching rationale for those guardrails is to uphold ethical principles. Therefore, comprehensive ethical stewardshipis the key driver.
References:
ISACA,AAIA Exam Content Outline- Domain 5: Ethical and Legal Considerations in AI (ethical AI principles, beyond compliance).
ISACA AI ethics and governance guidance emphasizing proactive, values-driven AI oversight.
NEW QUESTION # 59
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