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ISACA AAIA Exam Syllabus Topics:

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

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ISACA Advanced in AI Audit Sample Questions (Q317-Q322):

NEW QUESTION # 317
From a data appropriateness and bias perspective, which of the following should be of GREATEST concern when reviewing an AI model used in a credit scoring system?

A. The model utilizes historical credit data to predict future credit behavior.
B. The model uses postal codes as a primary factor in determining creditworthiness.
C. The model considers the applicant's income level as a key factor in the credit decision.
D. The model incorporates the applicant's loan history to assess spending habits.

Answer: B

Explanation:
Using postal codes as a primary factor in credit scoring raises concerns of geographic and socioeconomic bias. Postal codes can serve as proxies for race, ethnicity, or income level-- potentially violating fair lending laws and ethical guidelines.
"Auditors should flag models that rely on proxies for protected attributes. Postal codes are high- risk features that may inadvertently lead to redlining or other discriminatory practices."

NEW QUESTION # 318
From an audit perspective, which of the following BEST supports the objectives of an AI governance program?

A. Developing an AI risk register to record potential algorithmic bias
B. Defining AI policies for model interpretability, transparency, and resiliency
C. Establishing an AI ethics committee or responsible AI review board
D. Defining roles, responsibilities, and training for all stakeholders on AI ethics

Answer: B

Explanation:
While roles (Option A), committees (Option B), and registers (Option C) are essential organizational components, the " AI Policies " themselves are the most comprehensive support for governance objectives.
Policies provide the " enforceable standards " for how AI must be built and managed. Specifically, policies addressing interpretability, transparency, and resiliency ensure that the organization's AI systems are auditable, fair, and stable. According to ISACA, the governance program must be rooted in these technical and ethical pillars to provide the necessary guardrails for AI development. Policies act as the " Source of Truth " that guides committees and stakeholders in their decision-making processes.

NEW QUESTION # 319
Which of the following is MOST important to review in order to gain assurance that an AI model is performing without biases?

A. AI model temperature
B. AI development environment
C. AI training data
D. AI model adaptability

Answer: C

Explanation:
Bias in AI models is most commonly introduced through the training data. The AAIA™ Study Guide highlights that to ensure fairness, auditors and developers must evaluate the diversity, representativeness, and quality of the data used to train the model.
"The greatest source of bias in AI comes from the training data. Reviewing and auditing this data is critical to ensuring that outputs do not disproportionately affect specific groups or skew results." While adaptability (C) and model parameters like temperature (D) affect behavior, they do not address the root cause of most biases. The development environment (B) supports infrastructure but not ethical assurance.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Ethical and Legal Considerations in AI," Subsection: "Bias and Fairness in AI Systems"

NEW QUESTION # 320
Which of the following is MOST important for an IS auditor to consider when identifying AI risk in a know your customer (KYC) application within a banking organization?

A. Business disruption and financial impact
B. Benchmarking against peer organizations
C. Incident response plan
D. Intellectual property leakage and invalidation

Answer: A

Explanation:
In high-stakes financial applications like KYC, the primary concern is the potential business and regulatory impact of an AI error--such as false customer rejection or failure to detect fraudulent accounts. The AAIATM Study Guide emphasizes aligning AI risk assessments with business impact and regulatory exposure.
"In financial institutions, the most material risk of AI errors lies in operational disruption and regulatory fines. KYC models must be assessed for how errors can lead to compliance failures or reputational harm."

NEW QUESTION # 321
An IS auditor is looking to expedite reporting for an audit with complex issues. Which of the following would be the MOST effective way for the auditor to use generative AI?

A. Revising audit background and scope information based on new information from management
B. Developing a draft of an executive summary based on detailed findings and audit scope
C. Developing action items discussed in closing meetings for management action plans
D. Revising audit conclusions with precise verbiage to describe the audit observations

Answer: B

NEW QUESTION # 322
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