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ISACA AAIA Exam Overview:

Certification Vendor:	ISACA
Exam Name:	ISACA Advanced in AI Audit
Exam Number:	AAIA
Related Certifications:	CISA FCCA ACCA CIA CPA
Real Exam Qty:	90
Exam Format:	Computer-Based, Remotely Proctored, Multiple Choice
Exam Duration:	120 minutes
Available Languages:	English
Passing Score:	65%
Sample Questions:	ISACA AAIA Sample Questions
Exam Way:	Online remotely proctored computer-based exam
Pre Condition:	Candidates must hold an active CISA certification or another qualified audit-related designation such as CIA, CPA, ACCA, FCCA, Canadian CPA, Australian CPA/FCPA, or Japanese CPA.
Official Syllabus URL:	https://www.isaca.org/credentialing/aaia

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ISACA AAIA Exam Syllabus Topics:

Topic	Details
Topic 1	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.
Topic 2	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 3	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.

ISACA Advanced in AI Audit Sample Questions (Q28-Q33):

NEW QUESTION # 28
The PRIMARY objective of machine learning (ML) in data processing is to:

A. Perform actions that would typically require human intelligence.
B. Draw statistical inferences for creating artificial human intelligence.
C. Analyze data sets to identify visual patterns and trends.
D. Enhance the explainability of AI model outputs.

Answer: A

NEW QUESTION # 29
An IS auditor is testing an AI model used for determining insurance premiums and eligibility. Which of the following is the MOST effective testing method to identify bias in algorithm outputs?

A. Predictive analytics
B. Disparate impact analysis
C. Regression testing
D. Cross-cluster analysis

Answer: B

Explanation:
Disparate impact analysisis a specific technique used to detect whether model decisions disproportionately disadvantage certain protected or sensitive groups (e.g., by gender, age, ethnicity, or other attributes). For an AI model determiningpremiums and eligibility, fairness and non-discrimination are critical regulatory and ethical requirements, and AAIA content highlights fairness and bias evaluation as core elements of AI governance and risk management.
Regression testing (A) checks that changes do not introduce defects in previously functioning components, not fairness. Cross-cluster analysis (B) may reveal patterns but is not inherently a fairness test. Predictive analytics (D) is a broad term for forward-looking analysis, not a method specifically designed to detect bias.
Therefore,disparate impact analysisis the most appropriate and targeted method to identify bias in the insurance AI model's outputs.
References:
ISACA,AAIA Exam Content Outline- Domain 1: AI Governance and Risk (bias, fairness, and transparency considerations).
ISACA AI ethics and fairness guidance used in AAIA training materials.

NEW QUESTION # 30
A company deploys a third-party AI vendor's model without visibility into its training data or architecture. What is the PRIMARY audit concern?

A. Vendor lock-in
B. Higher licensing costs
C. Slower inference speed
D. Lack of transparency creating an inability to assess bias, drift, or explainability

Answer: D

NEW QUESTION # 31
Which of the following is the GREATEST benefit of using large language models (LLMs) to identify potential control deficiencies?

A. Summarizing multiple process narratives for inconsistencies
B. Allowing the auditor to replace walkthroughs with automated testing
C. Independently verifying audit options based on control assessments
D. Simulating financial transactions for internal control testing

Answer: A

Explanation:
LLMs excel at processing large amounts of unstructured text (e.g., policies, process narratives, procedures). The GREATEST benefit is their ability to summarize and compare multiple documents to identify inconsistencies (B), which can signal potential control weaknesses or misalignments. This aligns with AAIA's description of AI-enabled audit analytics that support document analysis, narrative comparison, and pattern detection for control assessment.

NEW QUESTION # 32
Which of the following considerations should be prioritized when using an AI tool to select a sample for conducting an audit of a financial institution's transaction processing system?

A. The ability to process large volumes of data
B. The speed of sample generation
C. The transparency of the sampling process
D. The historical performance in previous audits

Answer: C

Explanation:
In an audit context,transparencyof sampling is essential for demonstrating that the sample is fair, unbiased, and aligned with the audit objectives. When an AI tool selects samples for testing financial transactions, auditors must be able to explain and defendhowthe sample was generated-particularly to management, regulators, and external stakeholders. Option A directly supports AAIA's focus onaudit planning, sampling methodologies, and AI audit evidence.
High throughput (option B) and speed (option C) are beneficial but secondary to methodological soundness and explainability. Option D (historical performance) can be helpful but does not guarantee current transparency or appropriateness in new contexts. For AI-enabled sampling, the priority is that theselection logic is understandable, documented, and reproducible, ensuring audit defensibility.
References:
ISACA,AAIA Exam Content Outline- Domain 3: AI Auditing Tools and Techniques (Audit Testing and Sampling Methodologies; Audit Evidence Collection Techniques).
ISACA auditing guidance on sampling and transparency in AI-assisted audit procedures.

NEW QUESTION # 33
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