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ISACA AAIA Exam Overview:

Certification Vendor:ISACA
Exam Name:ISACA Advanced in AI Audit
Exam Number:AAIA
Related Certifications:CIA
FCCA
CPA
ACCA
CISA
Passing Score:65%
Available Languages:English
Exam Format:Multiple Choice, Computer-Based, Remotely Proctored
Real Exam Qty:90
Exam Duration:120 minutes
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:

TopicDetails
Topic 1
  • 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 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 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.

ISACA Advanced in AI Audit Sample Questions (Q94-Q99):

NEW QUESTION # 94
Which of the following is the PRIMARY benefit of implementing a robust data governance framework specific to AI solutions in an organization?

Answer: D

Explanation:
According to the AAIATM Study Guide, a robust data governance framework ensures that AI systems are compliant with data protection laws, ethical standards, and internal policies. It provides controls over data quality, access, retention, and processing, all of which are essential to avoid breaches and maintain trust.
"A strong data governance structure is foundational for regulatory compliance and ethical AI practices. It ensures that data privacy, integrity, and usage rights are maintained across the AI lifecycle."


NEW QUESTION # 95
Which of the following controls would MOST effectively mitigate worst-case service disruption scenarios affecting an AI-based application system?

Answer: C


NEW QUESTION # 96
An IS auditor is auditing a financial system in which a generative AI tool is used to identify trends in batches of 4,000 rows, while the generative AI tool has a limit of 3,000 tokens. Which of the following is the GREATEST concern?

Answer: A

Explanation:
Generative AI tools such as large language models often have token limitations that restrict how much input they can process in a single prompt. According to the AAIATM Study Guide, when input exceeds token limits, the model processes only the initial portion--leading to biased outputs based on early entries.
"Token limits in generative AI systems may lead to partial input processing, skewing model outputs toward the beginning of the data and introducing interpretive bias. Auditors must assess whether output reflects the full dataset."


NEW QUESTION # 97
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)?

Answer: D

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. Prompting the AI to check its own code (Option C) is unreliable because the model may suffer from the same biases or logic errors that created the code in the first place.


NEW QUESTION # 98
Which of the following should be done FIRST when developing an incident management process for AI threats?

Answer: B

Explanation:
The AAIA framework states that incident response begins withroles and responsibilities. Without clearly assigned accountability, no classification, escalation, or detection procedures can be effectively implemented.
Defining roles ensures:
* Ownership of monitoring
* Chain of command for incident decisions
* Clear responsibility for documentation
* Communication pathways
* Allocation of resources for containment
Classification (A), escalation (D), and SIEM configuration (C) follow AFTER roles are assigned. Therefore, defining roles and responsibilities is foundational.
References:
AAIA Domain 2: AI Incident Management
AAIA Domain 1: Governance and Accountability Structures


NEW QUESTION # 99
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