ISACA AAIA Pdf Format Practice Program

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ISACA AAIA Exam Overview:

Certification Vendor:ISACA
Exam Name:ISACA Advanced in AI Audit (AAIA) Exam
Exam Number:AAIA
Real Exam Qty:55
Related Certifications:CPA
CIA
CISA
Exam Duration:120 minutes
Passing Score:65%
Exam Price:USD 459 (member), USD 599 (non-member)
Exam Format:Computer-based, Remote proctoring or test center, Closed-book, Multiple-choice
Available Languages:English
Certificate Validity Period:Not publicly specified (requires ongoing CPE maintenance after certification)
Recommended Training:ISACA AI Audit Training Resources
Official AAIA Training Course Providers
Exam Registration:ISACA AAIA Certification Page
Sample Questions:ISACA AAIA Sample Questions
Exam Way:Computer-based exam delivered via PSI test centers or remote proctoring
Pre Condition:Must hold an active CISA, CIA, CPA, or equivalent ISACA-approved advanced auditing certification
Official Syllabus URL:https://www.isaca.org/credentialing/aaia

>> AAIA Reliable Test Syllabus <<

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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.

ISACA Advanced in AI Audit Sample Questions (Q124-Q129):

NEW QUESTION # 124
Which of the following will provide the BEST evidence to support the alignment of an AI model with an organization's business objectives?

Answer: A


NEW QUESTION # 125
Which of the following techniques BEST enhances the security and robustness of AI models against adversarial attacks?

Answer: D

Explanation:
Defensive distillation is a specialized technique designed to make an AI model more robust against adversarial " perturbations " -small changes to input data intended to trick the model. It works by training a smaller model to mimic the probability distributions of a larger, pre-trained model, which effectively " smooths " the decision boundaries and makes it harder for attackers to find exploitable gaps. While access reviews and awareness training are standard security controls, they do not address the mathematical vulnerabilities inherent in ML algorithms. Defensive distillation specifically targets the model ' s resilience to technical exploitation.


NEW QUESTION # 126
Which of the following testing techniques would BEST validate whether an organization's data governance program effectively ensures data quality and integrity for AI model training and deployment?

Answer: D

Explanation:
Assessing data lineage provides insight into the origin, flow, and transformation of data across its lifecycle, which is crucial for validating data governance. The AAIA™ Study Guide states that data lineage is essential to ensure the accuracy, consistency, and trustworthiness of data used in training AI models.
"Traceability of data sources is a core tenet of effective data governance. Data lineage validation ensures data quality, prevents unauthorized modifications, and maintains auditability." BIA (A) focuses on impact, not data quality. Reviewing SDLC (B) is broad and may not highlight data- specific risks. Penetration testing (C) addresses security, not governance. Therefore, D is the best method.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Governance and Risk Management," Subsection: "Data Quality, Integrity, and Governance Practices"


NEW QUESTION # 127
Which of the following should be of GREATEST concern to an IS auditor when reviewing ethical considerations for an AI solution?

Answer: C

Explanation:
TheGREATEST concernis when the AI system'sdecision-making process is unexplainable(A), especially for high-impact or regulated decisions. AAIA stresses that explainability is essential for accountability, fairness assessments, compliance, and public trust. If decisions cannot be explained, the organization cannot validate fairness, detect bias, or justify outcomes to regulators or affected individuals.
Cloud hosting (B) is manageable through standard controls. Retraining frequency (C) affects performance but not core ethics. Draft documentation (D) is a procedural issue, not an ethical barrier. Unexplainable decision logic is thefoundational ethical risk.
References:
ISACA,AAIA Exam Content Outline- Domain 5: Ethical and Legal Considerations in AI (explainability, accountability).


NEW QUESTION # 128
An IS auditor uses an internally developed generative AI tool to prepare a status update for audit stakeholders. Which of the following is the auditor's MOST appropriate course of action?

Answer: C

Explanation:
Auditors using AI tools to generate reports or updates must ensure the output is reliable, factually accurate, and complete. As per the AAIATM Study Guide, accountability for audit content remains with the auditor, even when generative AI assists with content creation. Therefore, verifying the completeness and correctness of AI-generated content is the primary responsibility.
"AI-generated audit outputs must be validated against source data and professional standards.
The auditor must critically assess AI contributions and not rely on them without human oversight."


NEW QUESTION # 129
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