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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.

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Actual ISACA AAIA Exam Questions And Correct Solution

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ISACA Advanced in AI Audit Sample Questions (Q151-Q156):

NEW QUESTION # 151
Which of the following is the BEST approach for auditing an AI system's compliance with the
"right to explanation" under applicable privacy regulations?

Answer: D

Explanation:
The right to explanation (e.g., under GDPR Article 22) requires organizations to be able to provide individuals with meaningful information about automated decisions affecting them -- auditors should test whether this capability actually exists and functions.


NEW QUESTION # 152
Which of the following BEST ensures that an AI system complies with user data ownership rights under privacy regulations?

Answer: D


NEW QUESTION # 153
When auditing the transparency of an AI system, which of the following would be the MOST effective way to understand the model's decision-making process?

Answer: B

Explanation:
Transparency in AI systems is a key requirement to ensure trust, accountability, and ethical compliance. According to the ISACA AAIATM Study Guide under the "AI Governance and Risk Management" section, understanding the decision-making process of an AI system falls under the principle of explainability. Explainability refers to the degree to which an observer can understand the internal mechanics of an AI system and the rationale behind its outputs.
"Reviewing the explainability of AI outputs allows auditors and stakeholders to determine whether model decisions are interpretable and justifiable. High transparency means stakeholders can trace how and why a decision was made." While algorithm complexity and computational cost are technical considerations, they do not directly facilitate the audit of decision-making transparency. Similarly, training data diversity is essential for bias reduction but does not explain how decisions are derived. Therefore, option D is the most aligned with auditing transparency.


NEW QUESTION # 154
Which of the following is the BEST practice for documenting an AI system's intended use case and known limitations for stakeholders?

Answer: D

Explanation:
Model/system cards provide a standardized, accessible, and maintainable format for documenting intended use, performance characteristics, limitations, and risks -- essential for governance and audit trails.


NEW QUESTION # 155
An IS auditor notes that the combined number of records in the training, validation, and testing sets exceeds the total number of records in the original data set. What is the GREATEST risk?

Answer: B

Explanation:
In a proper AI pipeline, the original data must be " strictly partitioned " into disjoint sets. If the sum of the subsets exceeds the original count, it means some records were " Reused " across sets. This leads to " Data Leakage, " where the model " sees " the test or validation data during the training phase. As a result, the model ' s performance metrics will be artificially inflated, giving a false sense of accuracy. The ISACA AAIA™ Study Guide identifies data leakage as a primary cause of " Model Evaluation Failure, " rendering all testing results invalid.


NEW QUESTION # 156
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