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ISACA AAIA Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: AI Operations	46%	- Operational Controls and Readiness - Third-Party and Supply Chain Risk - Incident Management and Resilience - AI System Lifecycle and Deployment - Performance Monitoring and Evaluation
Topic 2: AI Auditing Tools and Techniques	21%	- Audit Testing and Sampling Methodologies - Data Quality and Analytics for AI Audit - Audit Evidence Collection Techniques - AI Audit Outputs and Reporting - Audit Planning and Design
Topic 3: AI Governance and Risk	33%	- AI Models, Considerations, and Requirements - AI Risk Management - AI Governance and Program Management - Ethics, Regulations, and Standards for AI - Privacy and Data Governance Programs

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ISACA Advanced in AI Audit Sample Questions (Q117-Q122):

NEW QUESTION # 117
During a pre-implementation risk assessment, an AI model is determined to present a significant risk of bias and potential harm in excess of the organization's risk tolerance. Which of the following is the MOST appropriate response?

A. Postpone deployment until the risk can be safely managed.
B. Obtain board approval for an exception.
C. Revisit the risk tolerance to ensure it is appropriate.
D. Enhance the data that the model is trained on.

Answer: A

Explanation:
The AAIATM Study Guide advises that if an AI model presents a risk that exceeds the organization's predefined risk tolerance--especially in cases of ethical harm or bias--deployment should be delayed until proper safeguards are in place. This approach prevents legal exposure and preserves stakeholder trust.
"When AI risks exceed acceptable thresholds, organizations must suspend implementation until corrective action reduces the risk to within tolerance levels. Proceeding without mitigation violates sound governance principles."

NEW QUESTION # 118
Which of the following is the GREATEST concern when an audit team relies on generative AI to create audit reports?

A. The reports may use inconsistent formatting from prior audit findings.
B. The reports may be more likely to reflect outdated information.
C. The reports may tend to use generic language for audit issues.
D. The reports may contain misstatements resulting from hallucinations.

Answer: D

Explanation:
The greatest concern is that the generative model may hallucinate, producing incorrect facts or conclusions (option B). In an audit context, hallucinations can create false statements about control effectiveness, misreport risks, or incorrectly summarize evidence.
AAIA stresses that auditors must maintain professional skepticism and validate AI-generated content.
Misstatements are high-risk because they undermine audit credibility, regulatory compliance, and organizational decision-making.
Formatting inconsistency (C) and generic language (D) are cosmetic issues. Outdated information (A) is a concern but does not inherently create false conclusions.
Hallucinated misinformation is the most severe and dangerous issue in AI-generated audit reporting.
References:
AAIA Domain 3: AI in Audit Processes (accuracy of AI outputs, hallucination risks).
AAIA Domain 5: Ethical Responsibilities in AI-Assisted Work.

NEW QUESTION # 119
An IS auditor is reviewing change documentation of an AI model. Which of the following would pose the GREATEST risk?

A. There is a low number of test scenarios.
B. Test results are not in a standardized format.
C. Management has not tested the model.
D. The seed used for testing is not documented.

Answer: A

Explanation:
A low number of test scenarios (C) indicates insufficient testing coverage, creating a high likelihood that errors, biases, or edge-case failures remain undetected. AI systems require broad, diverse scenario testing, including fairness, robustness, and stress testing. AAIA highlights comprehensive testing as essential to AI reliability and risk mitigation.

NEW QUESTION # 120
An AI tool is being implemented for a regional healthcare organization. Which of the following training methods BEST ensures the AI output does not reveal whether someone's personal data was used?

A. Differential privacy applied during model training
B. Supervised learning with labeled patient records
C. Data augmentation during training to improve privacy
D. Transfer learning using public health data sets

Answer: A

Explanation:
Differential privacy introduces carefully calibrated noise during training or query responses so that it becomes mathematically difficult to infer whether any specific individual's record is included in the training set. For healthcare data--highly sensitive and subject to strict privacy laws--this technique directly supports privacy-by-design, reducing the risk that model outputs leak membership information or reconstruct personal records.

NEW QUESTION # 121
Which control BEST mitigates the risk of a machine learning model being manipulated through adversarial input perturbations?

A. Role-based access control on the training pipeline
B. Regular password rotation
C. Adversarial robustness testing during model validation
D. Data encryption at rest

Answer: C

Explanation:
Adversarial robustness testing deliberately crafts inputs designed to fool the model, allowing teams to measure and harden against evasion attacks before deployment.

NEW QUESTION # 122
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