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ISACA AAIA Exam Syllabus Topics:

TopicDetails
Topic 1
  • AI Operations: It covers managing AI-specific data needs—including collection, quality, security, and classification—applying development lifecycle methodologies with privacy and security by design, change and incident management, testing AI solutions, identifying AI-related threats and vulnerabilities, and supervising AI deployments.
Topic 2
  • AI GOVERNANCE AND RISK: It encompasses understanding different AI models and their life cycles, guiding AI strategy, defining roles and policies, managing AI-related risks, overseeing data privacy and governance, and ensuring adherence to ethical practices, standards, and regulations.
Topic 3
  • Auditing Tools and Techniques: This section of the exam measures the skills of AI auditors and centers on auditing AI systems using appropriate tools and methods. It includes audit planning and design, sampling methodologies specific to AI, collecting audit evidence, using data analytics for quality assurance, and producing AI audit outputs and reports, including follow-up and quality control measures.

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ISACA Advanced in AI Audit Sample Questions (Q187-Q192):

NEW QUESTION # 187
An organization seeks to sustain effective AI governance and risk management amid rapidly evolving AI technologies. Which of the following represents the MOST effective course of action?

Answer: D

Explanation:
Sustaining effective AI governance and risk management requires continuous, organization-wide awareness
, not just one-off or role-limited training. Option D embeds AI topics (governance, risk, ethics, privacy, and security) into the existing security awareness program , which is already a recurring and mandatory mechanism across the enterprise. This supports ongoing adaptation to rapidly evolving AI technologies, aligns with ISACA's emphasis on integrating AI risk considerations into existing governance and risk frameworks, and ensures that staff at all levels understand their responsibilities.
Options A and C are too narrow in scope, as they target only technical staff or senior management; they help but do not create pervasive, sustainable governance. Option B can supplement internal training, but outsourcing alone does not ensure continuity or alignment with internal policies.
References:
ISACA, AAIA Exam Content Outline - Domain 1: AI Governance and Risk (AI governance, AI training and awareness, program metrics).
ISACA, Advanced in AI Audit Exam Candidate Guide - sections on governance, risk, and professional responsibilities.


NEW QUESTION # 188
While evaluating a complex machine learning (ML) model used for regulatory compliance in a financial institution, which of the following should the IS auditor do to BEST ensure transparency?

Answer: A

Explanation:
Transparency in AI, especially in regulated sectors like finance, is best achieved by using explainability tools that interpret the internal workings and outputs of complex ML models. The AAIATM Study Guide highlights model explainability as essential for both auditors and regulators to understand why certain decisions are made.
"Explainability tools allow auditors to trace predictions back to input features, helping verify fairness, logic, and compliance. These tools support transparency and trustworthiness in black- box models."


NEW QUESTION # 189
An organization plans to share customer data collected through an AI system with third-party vendors. Which of the following BEST demonstrates compliance with data privacy principles?

Answer: B

Explanation:
The strongest demonstration of compliance with privacy principles--especially those emphasized in AAIA, such as lawfulness, transparency, and consent--is obtaining expressed customer consent (B) before sharing data with third parties. Consent ensures that data processing aligns with legal and ethical requirements, reducing risks related to regulatory violations, unauthorized processing, and loss of customer trust.


NEW QUESTION # 190
In the context of an AI implementation, which of the following actions is MOST critical for an organization's change management program?

Answer: A

Explanation:
The AAIA™ Study Guide emphasizes that AI implementations introduce dynamic and non-deterministic elements into systems, increasing the risk associated with changes. A comprehensive, AI-specific risk assessment is therefore the most critical component of a change management program to ensure that updates, retraining, or parameter adjustments do not introduce vulnerabilities or unintended consequences.
"Risk assessments tailored to AI are crucial because changes to models, training data, or infrastructure can affect performance, ethical compliance, or expose the system to new threats. A standard IT change review is often insufficient." While having a governance committee (A) and reviewing documentation (B) are important supporting practices, only option C directly mitigates the core risks of AI system change. Ethics training (D) supports awareness but is not directly tied to change control.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Governance and Risk Management," Subsection: "Change Management and AI Risk Control"


NEW QUESTION # 191
Which of the following is the BEST use of AI to audit relationships for conflicts of interest or collusion?

Answer: A

Explanation:
Graph analytics is specifically designed to analyze complex relationships among people, entities, transactions, and systems. According to AAIA audit methodologies, graph analytics helps identify hidden or non-obvious relationships indicative of:
* Collusion
* Fraud rings
* Undisclosed conflicts of interest
* Influence networks
* Hidden ownership structures
Correlation matrices (A) only measure linear relationships. Time series (B) identifies patterns over time, not relationships. Monte Carlo simulation (D) models uncertainty but does not uncover relational structures.
Graph analytics is the strongest AI-enabled method for mapping and auditing relational risks.
References:
AAIA Domain 3: AI Tools for Audit Analytics
AAIA Domain 4: Relationship Analysis and Fraud Detection


NEW QUESTION # 192
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