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ISACA Advanced in AI Audit Sample Questions (Q88-Q93):

NEW QUESTION # 88
When auditing a research agency's use of generative AI models for analyzing scientific data, which of the following is MOST critical to evaluate in order to prevent hallucinatory results and ensure the accuracy of outputs?

Answer: D


NEW QUESTION # 89
When an IS auditor reviews audit evidence gathered by AI, which of the following practices should the auditor perform to ensure the reliability of the evidence?

Answer: B

Explanation:
Professional skepticism is a core requirement for auditors using AI tools. Because AI can suffer from " hallucinations " or misinterpretation of data, the auditor must " Validate the evidence against source documents. " This involves taking a sample of the AI ' s findings and manually verifying them against the original records (e.g., invoices, contracts, or logs). According to the ISACA AAIA™ Study Guide, while reviewing model cards (Option A) provides context on the tool ' s capabilities, it does not confirm that a specific piece of evidence is accurate. Direct verification against the " Ground Truth " (source documents) is the only way to ensure the reliability and integrity of AI-generated audit evidence.


NEW QUESTION # 90
Which of the following techniques is MOST appropriate for model hyperparameter tuning?

Answer: C

Explanation:
Hyperparameter tuning is the process of finding the optimal settings (like learning rate or depth) for an AI model. " Bayesian optimization " is an efficient, automated technique that builds a probability model of the objective function to select the most promising hyperparameters to test next. Unlike simple " grid search, " it saves significant time and computational cost. F1 score analysis is a way to measure the result of the tuning, but it is not the tuning method itself. Bayesian optimization is favored in advanced AI development for its ability to find optimal configurations in complex, multi-dimensional spaces.


NEW QUESTION # 91
Which of the following is the BEST way to support the development and design of high-risk AI systems?

Answer: A

Explanation:
The AAIA™ Study Guide emphasizes that the foundation of any high-performing and ethical AI system lies in the quality and integrity of its data. For high-risk AI systems, such as those used in healthcare, finance, or criminal justice, it is essential to base models on trustworthy data. This ensures reliable predictions, reduces bias, and mitigates risk.
"Trustworthy datasets are characterized by accuracy, completeness, consistency, and ethical sourcing. In high- risk AI applications, ensuring data quality at every stage is crucial to system reliability and compliance." While backups, user training, and MFA are important for security and operational resilience, they do not address the core challenge of ensuring model accuracy and fairness at the development and design phase.
Therefore, option C is the most effective practice.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Fundamentals and Technologies," Subsection: "Data Governance and Management"


NEW QUESTION # 92
An organization is training a skin cancer recognition model. Photographs collected from which of the following sources would present the GREATEST risk associated with data integrity?

Answer: C

Explanation:
Images fromsocial media platforms(C) pose the greatest risk todata integritybecause:
* Images may be heavily filtered or edited
* Lighting, scale, and resolution are inconsistent
* Metadata is unreliable
* Medical diagnosis labels (if any) are unverified
* Context is unknown and may include misinformation
* Sources cannot be validated or authenticated
AAIA stresses that medical AI systems requirehigh-quality, clinically validated datasetswith clear provenance, accuracy, and consent.
Research facilities (A) and dermatologist cohorts (D) provide medically reliable, high-integrity data.
Open-source augmentation files (B) are synthetic and secondary, but still safer than uncontrolled, unlabeled social media images.
References:
AAIA Domain 2: Data Quality, Provenance, Accuracy, and Label Integrity
AAIA Domain 1: Risk Identification in High-Impact AI Systems


NEW QUESTION # 93
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