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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 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
  • 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.

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ISACA Advanced in AI Audit Sample Questions (Q11-Q16):

NEW QUESTION # 11
An AI audit reveals that a loan approval model has a significantly higher rejection rate for a specific demographic group. What should be management's PRIMARY response?

Answer: A

Explanation:
A significantly higher rejection rate is a clear indicator of potentialalgorithmic discrimination. Management' s PRIMARY response should be to conduct acomprehensive bias analysis(C), including fairness metrics, root-cause analysis, model explainability assessments, and data quality reviews. AAIA prioritizes fairness auditing and bias remediation as central to AI governance.
Option A is unacceptable because fairness issues fall outside most risk tolerances. Option B is a procedural check, not the solution. Option D (synthesizing data) might help butonly afterthe root cause is identified-it is not the primary first step.
References:
ISACA,AAIA Exam Content Outline- Domain 1: Bias, Fairness, and Transparency Evaluations.


NEW QUESTION # 12
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: A

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 AAIATM 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."


NEW QUESTION # 13
Which of the following is the BEST way to test for proxy discrimination in a model that does not directly use a protected attribute (e.g., race)?

Answer: D

Explanation:
Proxy discrimination occurs when non-protected features correlate strongly with a protected attribute, effectively reintroducing bias. Correlation and outcome analysis on such features helps detect this.


NEW QUESTION # 14
A car manufacturer uses an AI model to predict maintenance needs for its vehicles. Which of the following techniques can an IS auditor apply to MOST effectively verify the AI model's decisions to stakeholders?

Answer: D


NEW QUESTION # 15
Which of the following is the MOST important reason to perform regular ethical reviews of AI systems?

Answer: A


NEW QUESTION # 16
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