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ISACA AAIA Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 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.

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ISACA Advanced in AI Audit Sample Questions (Q245-Q250):

NEW QUESTION # 245
An organization has exhausted its internal data sources to train an AI model. Which of the following is the BEST source to obtain new data?

Answer: A

Explanation:
When seeking external data for AI training, organizations must navigate significant legal and ethical risks regarding Intellectual Property (IP). "Copyright-free data" (or data used under clear licensing) is the safest and most ethical source. Using "web-scraped" data (Option A) without permission often leads to copyright infringement lawsuits and regulatory violations (e.g., using personal data without consent). "Shadow data" (unmanaged internal data) poses security risks.
For long-term sustainability and audit compliance, using properly licensed or public-domain data ensures the model's outputs do not violate the IP rights of third parties.


NEW QUESTION # 246
When an IS auditor is reviewing results from an AI system, which of the following would cause the GREATEST risk?

Answer: D


NEW QUESTION # 247
The accuracy of an AI model used for product recommendations, trained on data from a specific year, is declining two years later as customer buying patterns have shifted toward sustainable products. Which of the following is the MOST likely cause?

Answer: D

Explanation:
Data drift (also known as model drift or concept drift) refers to the phenomenon where the statistical properties of the target variable or the input data change over time, leading to a decline in model performance.
In this scenario, the fundamental shift in customer preferences toward sustainability represents a change in the real-world environment that the model ' s original training data no longer reflects. As stated in the AAIA™ guidelines, models operating in dynamic environments like retail require continuous monitoring to detect when historical patterns are no longer predictive. Covariate shift (Option D) is a sub-type of drift specifically involving changes in the distribution of input features, but " Data drift " is the broader, most appropriate term for the observed performance degradation.


NEW QUESTION # 248
While evaluating a complex machine learning (ML) model used for regulatory compliance in a financial institution, which of the following should the IS auditor do to BEST ensure transparency?

Answer: B

Explanation:
Transparency in AI, especially in regulated sectors like finance, is best achieved by using explainability tools that interpret the internal workings and outputs of complex ML models. The AAIATM Study Guide highlights model explainability as essential for both auditors and regulators to understand why certain decisions are made.
"Explainability tools allow auditors to trace predictions back to input features, helping verify fairness, logic, and compliance. These tools support transparency and trustworthiness in black- box models."


NEW QUESTION # 249
An IS auditor is auditing an organization's data governance framework. The primary objective is to provide assurance that data management practices are standardized to support a trustworthy AI system. Which of the following should be the auditor's MOST important consideration?

Answer: D

Explanation:
Accountability for data management (option D) is the most crucial consideration. The AAIA™ Study Guide emphasizes that "clear roles, responsibilities, and ownership for data management activities are central to trustworthy AI systems, as they ensure compliance, traceability, and the consistent application of policies and controls." Retention, portability, and data training practices are important, but accountability is foundational for the enforcement and monitoring of all other governance practices.
Reference:ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Accountability in Data Governance for AI"


NEW QUESTION # 250
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