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ISACA AAIA Exam Overview:

Certification Vendor:ISACA
Exam Name:ISACA Advanced in AI Audit
Exam Number:AAIA
Related Certifications:CIA
ACCA
CISA
CPA
FCCA
Passing Score:65%
Exam Format:Computer-Based, Multiple Choice, Remotely Proctored
Available Languages:English
Exam Duration:120 minutes
Real Exam Qty:90
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 (Q249-Q254):

NEW QUESTION # 249
The PRIMARY objective of auditing AI systems is to:

Answer: A

Explanation:
The AAIATM Study Guide outlines the primary goal of auditing AI systems as ensuring that they operate ethically, transparently, and fairly. This includes identifying potential biases in decision- making and confirming that the rationale behind outcomes can be explained and understood.
"The key focus of an AI audit is to evaluate whether the system performs within the parameters of fairness, legality, and accountability. This involves testing for bias and ensuring decision-making transparency."


NEW QUESTION # 250
Which of the following AI system characteristics would BEST help an IS auditor evaluate the system's algorithm?

Answer: B

Explanation:
Transparency is a foundational principle for AI governance and effective risk management. When evaluating AI systems, IS auditors must assess not just how an algorithm functions, but also whether its processes and decisions are understandable and explainable. According to the ISACA Advanced in AI Audit™ (AAIA™) Study Guide, "algorithmic transparency-enabling auditors and stakeholders to see and understand the rationale behind automated decisions-is essential for ensuring accountability, fairness, and compliance with both organizational policies and regulatory requirements." Transparent justification of decisions (option C) directly supports an auditor's ability to evaluate the appropriateness, logic, and fairness of the AI's outputs. This is critical in identifying bias, ensuring ethical considerations are met, and verifying that outcomes are consistent with intended objectives. Without transparent justification, it is difficult for an IS auditor to independently assess whether decisions are being made based on valid, legal, and ethical grounds.
By contrast, options A, B, and D are either standard operational aspects or supportive features, but they do not directly empower auditors to evaluate or scrutinize the algorithm's decision-making process. The Study Guide explicitly highlights, "Effective AI governance frameworks require that systems provide clear, interpretable explanations for their decisions, supporting auditability and accountability across the AI lifecycle." Reference:ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Transparency and Explainability in AI Systems," Subsection: "Auditability and Accountability"


NEW QUESTION # 251
An AI audit tool incorrectly flagged that business decisions were biased, leading to inappropriate management action plans. Which of the following can BEST prevent this risk?

Answer: B

Explanation:
AI tools can produce " False Positives " in bias detection if they misinterpret the data. To prevent management from acting on incorrect audit findings, " Explainable AI (XAI) validation methods " (like SHAP or LIME) should be applied. XAI allows the auditor to see why the tool flagged a specific decision as biased.
If the tool ' s reasoning is flawed (e.g., it ignored a valid business justification), the auditor can correct the finding before it reaches management. This adds a necessary layer of " Auditor skepticism " and human validation to AI-driven audit insights.


NEW QUESTION # 252
An IS auditor uses an AI model to summarize worksheets, but several worksheets contained a
"Hidden Cell" instructing the model to ignore control failures. Which solution BEST mitigates the risk?

Answer: A

Explanation:
This is a "Data-based Prompt Injection," where the AI treats data as instructions. By "Allowing extraction only to predefined values and headers," the auditor restricts the AI's "Context Window" to specific, known data fields. This prevents the AI from "reading" and executing malicious commands hidden in other cells.


NEW QUESTION # 253
Which of the following sampling strategies would MOST likely involve the use of AI?

Answer: A

Explanation:
"Adaptive sampling" is a dynamic strategy where the selection of future samples depends on the results of previous samples. AI excels in this area by continuously analyzing incoming data and automatically focusing the "sample" on areas where risks or anomalies are surfacing (e.g., a fraud detection bot increasing its scrutiny on a specific merchant after one suspicious transaction).


NEW QUESTION # 254
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