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  2. ISACA Certification
  3. AAIA Exam
  4. ISACA.AAIA.v2026-06-21.q153 Dumps
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Question 111

Which of the following AI system characteristics would BEST help an IS auditor evaluate the system's algorithm?

Correct Answer: C
Transparency is a foundational principle for AI governance and effective risk management. When evaluating AI systems, IS auditors must assess not just how an algorithm functions, but also whether its processes and decisions are understandable and explainable. According to the ISACA Advanced in AI AuditTM (AAIATM) Study Guide, "algorithmic transparency--enabling auditors and stakeholders to see and understand the rationale behind automated decisions--is essential for ensuring accountability, fairness, and compliance with both organizational policies and regulatory requirements." Transparent justification of decisions (option C) directly supports an auditor's ability to evaluate the appropriateness, logic, and fairness of the AI's outputs. This is critical in identifying bias, ensuring ethical considerations are met, and verifying that outcomes are consistent with intended objectives. Without transparent justification, it is difficult for an IS auditor to independently assess whether decisions are being made based on valid, legal, and ethical grounds.
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Question 112

Which of the following is the GREATEST benefit of using AI to evaluate data quality during an audit?

Correct Answer: C
Traditional data quality checks often rely on manual sampling, which can miss rare but significant errors. AI-driven audit tools can analyze 100% of a population to "identify outliers" that represent data errors, anomalies, or potential fraud. According to the AAIATM framework, identifying these outliers is crucial because "dirty data" can fundamentally skew AI model predictions and audit conclusions. By automating the detection of data inconsistencies, auditors can focus their manual efforts on investigating high-risk items, thereby increasing both the accuracy of the audit and the overall reliability of the data used for model training.
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Question 113

When developing an audit plan, which of the following is MOST important specifically for the transparency of an AI application?

Correct Answer: A
Explainability testing is essential for determining how and why an AI model produces specific outputs.
Transparency is a key AAIA requirement, especially in regulated or high-impact environments.
Explainability testing ensures:
* Stakeholders understand model decisions
* Auditors can verify fairness and accuracy
* Regulators can review decision logic
* The organization can justify outcomes to affected individuals
* Models align with ethical and governance expectations
Regression testing (B) checks consistency across versions.
Compliance testing (C) checks legal adherence.
Validation testing (D) checks accuracy.
None specifically ensure transparency.
References:
AAIA Domain 5: Explainability, Transparency, Accountability
AAIA Domain 3: Auditability of AI Systems
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Question 114

Which of the following should be done FIRST when an attacker exfiltrates sensitive information from an AI model?

Correct Answer: B
According to the AAIATM Study Guide, the first action in response to a confirmed or suspected data exfiltration attack should be containment. Isolating impacted systems helps prevent further exploitation while allowing for a secure investigation of the breach source.
"The initial response to any AI-related data breach must prioritize containment of the threat.
Immediate isolation of affected systems helps mitigate further damage and supports a controlled forensic analysis."
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Question 115

An IS auditor is auditing an organization's data governance framework. The primary objective is to provide assurance that data management practices are standardized to support a trustworthy AI system. Which of the following should be the auditor's MOST important consideration?

Correct Answer: D
Accountability for data management (option D) is the most crucial consideration. The AAIATM Study Guide emphasizes that "clear roles, responsibilities, and ownership for data management activities are central to trustworthy AI systems, as they ensure compliance, traceability, and the consistent application of policies and controls." Retention, portability, and data training practices are important, but accountability is foundational for the enforcement and monitoring of all other governance practices.
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