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NEW QUESTION # 124
Which of the following would pose the GREATEST risk when reviewing AI acceptable use training content?
Answer: D
Explanation:
Thenon-deterministic nature of AIrefers to the fact that modern AI systems-especially generative AI, reinforcement learning, and probabilistic models-do not always produce the same output even when given identical inputs. According to AAIA's ethical and operational guidance, understanding non-determinism is essential for:
* Managing expectations of AI behavior
* Preventing overreliance on AI outputs
* Identifying hallucinations or inconsistent outcomes
* Ensuring human oversight remains in place
* Supporting proper audit trails and accountability
* Upholding professional skepticism toward AI-generated outputs
If training doesnotexplain non-determinism, employees may mistakenly believe AI outputs are always authoritative or correct, which can lead to unsafe reliance, ethical violations, and audit failures.
Other options (A, C, D) are helpful but not foundational. Lack of understanding of non-determinism fundamentally undermines safe and responsible AI use.
References:
AAIA Domain 5: Ethical and Legal Considerations(Explainability, Output Variability, Responsible Use).
AAIA Domain 3: Oversight and Human-in-the-Loop Principles.
NEW QUESTION # 125
An IS auditor notes that the combined number of records in the training, validation, and testing sets exceeds the total number of records in the original data set. What is the GREATEST risk?
Answer: C
Explanation:
In a proper AI pipeline, the original data must be " strictly partitioned " into disjoint sets. If the sum of the subsets exceeds the original count, it means some records were " Reused " across sets. This leads to " Data Leakage, " where the model " sees " the test or validation data during the training phase. As a result, the model ' s performance metrics will be artificially inflated, giving a false sense of accuracy. The ISACA AAIA™ Study Guide identifies data leakage as a primary cause of " Model Evaluation Failure, " rendering all testing results invalid.
NEW QUESTION # 126
Which of the following techniques BEST supports machine learning (ML) training in sentiment analysis?
Answer: B
Explanation:
Sentiment analysis is a classification task where text is categorized (e.g., positive, negative, or neutral). Logistic regression is a fundamental and highly effective statistical method for binary or multi-class classification, making it a standard choice for sentiment models. It estimates the probability of an input belonging to a specific class. While more complex architectures like Transformers (LLMs) are now common, logistic regression remains a benchmark for its simplicity and transparency in audit contexts. Image recognition and ANOVA are not relevant to the linguistic processing required for sentiment analysis, and the log-rank test is typically used in survival analysis rather than NLP classification.
NEW QUESTION # 127
An IS auditor is evaluating an organization's incident management program to ensure it is sufficiently prepared to manage AI-related incidents. Which of the following is MOST important for the auditor to validate?
Answer: A
Explanation:
AI-related incidents often differ significantly from traditional IT incidents due to their dependence on data, model behavior, and algorithm performance. According to the AAIA™ Study Guide, incident management programs must include capabilities specifically tailored to AI, such as detecting and mitigating model drift and safeguarding against data poisoning or integrity attacks.
"AI incident response frameworks must account for issues unique to machine learning, including model drift, adversarial inputs, and data integrity breaches. An effective program incorporates detection, response, and recovery mechanisms for these AI-specific threats." While options A and B contribute to improving incident response over time, and option D suggests best- practice alignment, only option C directly addresses active response capabilities for high-risk, real-time AI vulnerabilities.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Governance and Risk Management," Subsection: "Incident and Risk Management in AI Contexts"
NEW QUESTION # 128
For a sales promotion, an AI system sorts customer attributes into several categories by analyzing transaction history. Verifying which of the following would BEST validate the effectiveness of this process?
Answer: A
Explanation:
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 AAIATM 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."
NEW QUESTION # 129
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