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ISACA AAIA Exam Overview:

Certification Vendor:	ISACA
Exam Name:	ISACA Advanced in AI Audit
Exam Number:	AAIA
Real Exam Qty:	90
Passing Score:	450 (scaled, range 200–800)
Exam Format:	Scenario-based questions, Computer-based testing, Multiple-choice questions
Exam Duration:	150 minutes
Exam Price:	US$459 (member) / US$599 (non-member)
Related Certifications:	CIA CISA ACCA CPA FCCA
Certificate Validity Period:	3 years
Available Languages:	English, Spanish, Chinese
Recommended Training:	AAIA Review Manual AAIA Official Review Course
Exam Registration:	ISACA Official Registration
Sample Questions:	ISACA AAIA Sample Questions
Exam Way:	Computer-based; PSI test centers or remote proctored (remote not available in India, Mainland China, Hong Kong)
Pre Condition:	Hold active CISA, CIA, CPA, ACCA, FCCA or equivalent qualified certification with IT audit/advisory focus
Official Syllabus URL:	https://www.isaca.org/credentialing/aaia/aaia-exam-content-outline

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ISACA AAIA Exam Syllabus Topics:

Topic	Details
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	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 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 (Q298-Q303):

NEW QUESTION # 298
A generative AI system has a validation control in place to reject inappropriate questions by checking them against built-in ethical standards. Which of the following enables malicious actors to circumvent this control through prompt engineering?

A. Presenting theoretical situations to justify the reason for asking the questions
B. Submitting the same questions in a foreign language translated by another AI-based system
C. Asking the same questions later when the algorithm has changed after further learning
D. Randomly placing keywords unrelated to the main topic

Answer: A

Explanation:
Prompt engineering manipulation often involves disguising inappropriate questions in hypothetical or conditional formats, tricking the AI system into bypassing its filters. The AAIATM Study Guide highlights this as a common attack vector in generative AI misuse.
"Adversaries may reframe queries as academic or theoretical scenarios to exploit AI models' reasoning patterns, bypassing ethical constraints. Auditors should test for these circumvention techniques."

NEW QUESTION # 299
Which of the following insider threats involving the use of AI would present the GREATEST risk?

A. Leaking of system hyperparameters
B. Destroying system backups
C. Exfiltrating sensitive data
D. Launching social engineering attacks

Answer: C

Explanation:
The GREATEST insider threat is exfiltrating sensitive data (D). AI systems often contain rich datasets including personal, financial, operational, and proprietary information. If an insider extracts or leaks this data, the result can be severe legal, regulatory, and reputational consequences.

NEW QUESTION # 300
Which of the following is the BEST recommendation for an organization that has adopted "vibe coding" (using AI to generate code based on high-level natural language prompts)?

A. Build the organization's own cryptography.
B. Discontinue the current practice.
C. Adopt a security checklist for code review.
D. Prompt the AI to review its own code.

Answer: C

Explanation:
"Vibe coding" or AI-assisted development carries the risk of introducing "hallucinated" vulnerabilities or insecure coding patterns that the AI learned from public (and potentially flawed) repositories. The most responsible control is to implement a rigorous, human-led "security checklist" for code reviews. This ensures that every line of AI-generated code is checked for common flaws like SQL injection or hardcoded credentials.

NEW QUESTION # 301
Which of the following will provide the BEST evidence to support the alignment of an AI model with an organization's business objectives?

A. AI model vulnerability assessment
B. AI model inventory
C. AI change management requests
D. AI acceptable use policy

Answer: B

NEW QUESTION # 302
Which of the following types of AI can use unlabeled data sets to imitate human learning behavior?

A. Supervised learning
B. Unsupervised learning
C. Federated learning
D. Reinforcement learning

Answer: B

Explanation:
Unsupervised learningusesunlabeled datato discover patterns, structures, or groupings without explicit outcome labels. The model "learns" by identifying similarities, clusters, or latent structures within the data, somewhat analogous to how humans can notice patterns without being told the correct answer. In AAIA's fundamentals coverage, unsupervised methods (e.g., clustering, dimensionality reduction) are explicitly linked to situations wherelabels are unavailable or costly, yet insight is still needed.
Supervised learning (A) requires labeled examples (input-output pairs). Federated learning (B) describes a distributed training paradigm, not the labeling requirement itself. Reinforcement learning (C) uses feedback in the form of rewards and penalties rather than unlabeled static datasets. Therefore,unsupervised learningis the correct type that directly usesunlabeled datato learn structure.
References:
ISACA,AAIA Exam Content Outline- Domain 1: AI Models, Considerations, and Requirements (supervised, unsupervised, reinforcement learning).
ISACA AI fundamentals content on learning types and data labeling.

NEW QUESTION # 303
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