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[General] Three formats of ISACA AAIA practice exams meet the diverse needs

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【General】 Three formats of ISACA AAIA practice exams meet the diverse needs

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ISACA Advanced in AI Audit Sample Questions (Q175-Q180):NEW QUESTION # 175
An IS auditor identifies that an AI model occasionally invents nonexistent medical test results. Which of the following recommendations would BEST mitigate this risk?
  • A. Enabling frequency penalties on rare words
  • B. Increasing the model context
  • C. Increasing the temperature
  • D. Decreasing the top-p sampling
Answer: D
Explanation:
Inventing nonexistent medical test results is a form ofhallucinationcommonly associated with generative AI models.Reducing top-p sampling(A) restricts the model to choosing from the most probable next tokens, reducing randomness and preventing the generation of fabricated or implausible content. AAIA emphasizes configuration tuning (temperature, top-k, top-p) as critical controls for mitigating hallucinations.
Increasing context (B) helps provide more information but does not directly reduce hallucinations. Increasing temperature (C) would make hallucinationsworseby adding randomness. Frequency penalties (D) discourage repetition, not hallucinations. The strongest mitigation isreducing sampling randomnessvia reduced top-p.
References:
ISACA,AAIA Exam Content Outline- Domain 2: AI Operations (generative AI safeguards, hallucination mitigation).

NEW QUESTION # 176
Which of the following is the PRIMARY objective of performing adversarial testing on AI models?
  • A. Fostering security awareness
  • B. Validating AI incident response plans
  • C. Determining key risk indicators (KRIs)
  • D. Identifying control gaps
Answer: D
Explanation:
Adversarial testinginvolves simulating real-world attacks or malicious inputs against AI models (e.g., adversarial examples, poisoning, evasion) to identify how the system behaves under intentional misuse or hostile conditions. Theprimary objectiveis to discoverweaknesses and control gaps(D) in the model and its surrounding processes-such as inadequate input validation, insufficient monitoring, or missing safeguards against adversarial inputs.
While results from adversarial testing may inform incident response planning (A), KRI definition (B), or security awareness (C), those are secondary benefits. AAIA's coverage of AI threats and vulnerabilities emphasizes adversarial testing as acontrol validation and gap-identification mechanism, directly addressing AI-specific risk exposure.
References:
ISACA,AAIA Exam Content Outline- Domain 1 and Domain 2: Threats and Vulnerabilities Specific to AI; Testing Techniques for AI Solutions.
ISACA guidance on adversarial testing and AI security posture assessment.

NEW QUESTION # 177
Which of the following BEST detects model drift or unexpected changes in AI model outputs?
  • A. Anomaly monitoring
  • B. AI model retraining
  • C. Standardization of AI configurations
  • D. AI model documentation reviews
Answer: A
Explanation:
Anomaly monitoring detects irregularities or deviations in input data or model outputs, which are key indicators of model drift. According to the AAIA™ Study Guide, continuous anomaly detection is one of the most effective methods for identifying when a model is no longer functioning as expected due to changes in the data environment.
"Monitoring for output anomalies enables early identification of model drift. This proactive approach allows organizations to retrain or adjust models before significant performance degradation occurs." Configuration standardization (A) and documentation reviews (C) support governance but don't detect changes. Retraining (D) is a remediation step, not a detection mechanism. Therefore, B is correct.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Operations and Performance," Subsection: "Model Drift Detection and Anomaly Monitoring"

NEW QUESTION # 178
Which of the following do supervised AI learning models PRIMARILY use to train algorithms?
  • A. Randomized data sets
  • B. Clustered data sets
  • C. Unlabeled data sets
  • D. Labeled data sets
Answer: D

NEW QUESTION # 179
A retail organization uses an AI model to analyze customers' purchase history in order to offer personalized discounts. Which of the following practices represents the MOST ethical use of customer data?
  • A. Retaining and analyzing all available customer data to ensure unbiased recommendations
  • B. Sharing customer purchase data with third-party vendors to improve advertising and communication
  • C. Utilizing customer purchase data only after obtaining explicit consent and allowing customers to opt out
  • D. Providing the public with access to review and audit the data set of collected customer information
Answer: C
Explanation:
The ethical use of customer data is rooted in respecting privacy, maintaining informed consent, and enabling data subjects to exercise control over their personal information. The AAIA™ Study Guide clearly outlines that obtaining explicit consent and providing opt-out capabilities align with principles of data protection and ethical AI.
"Ethical AI implementation includes transparency in data collection, clear consent mechanisms, and the right of users to opt out or control their personal data usage. Retail and consumer applications must ensure that personalized services do not override these data subject rights." Options B and D violate principles of minimal data use and consent, while C may create unnecessary privacy exposure. Therefore, A is the correct and most ethical practice.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Ethical and Legal Considerations in AI," Subsection: "Informed Consent and Customer Privacy"

NEW QUESTION # 180
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