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ISACA AAIA Exam Syllabus Topics:

Topic	Details
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	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	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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ISACA Advanced in AI Audit Sample Questions (Q197-Q202):

NEW QUESTION # 197
An organization is evaluating change management practices for AI-based decision support models. Which of the following BEST demonstrates effective AI-focused change management?

A. Engaging an independent expert to review the model's accuracy and precision on a quarterly basis
B. Assigning a single data science team member to adjust the model in order to establish accountability
C. Deploying two separate copies of the model after each adjustment to compare results
D. Documenting model updates and retraining sessions to ensure traceability

Answer: D

NEW QUESTION # 198
What is the GREATEST risk of relying solely on aggregate accuracy as the primary success metric for a deployed AI model?

A. It can mask significant performance disparities across subgroups or specific use cases
B. It requires too much computing power to calculate
C. It is too difficult for auditors to understand
D. It cannot be calculated for classification models

Answer: A

Explanation:
Aggregate accuracy can be high overall while performance varies significantly for specific subpopulations or edge cases -- a key reason disaggregated and fairness-specific metrics are also needed.

NEW QUESTION # 199
Which of the following is the MOST essential attribute of an AI-driven audit tool?

A. Explainability of model conclusions
B. Use of labeled training datasets
C. Support for a range of statistical methods
D. Optimization of audit resources

Answer: A

Explanation:
According to the ISACA AAIA™ Study Guide, transparency is the cornerstone of AI auditing. For an auditor to rely on an AI-driven tool, the tool must provide " explainability " -the ability to describe how a specific conclusion or anomaly was identified. Without explainability, the AI remains a " black box, " preventing the auditor from exercising professional skepticism or justifying audit findings to stakeholders. While optimizing resources and supporting statistics are beneficial, they do not provide the necessary assurance that the model ' s logic is sound, unbiased, and compliant with regulatory standards. Auditors are required to validate the rationale behind automated decisions to ensure accountability.

NEW QUESTION # 200
An IS auditor observes that an AI-based fraud detection system used by an insurance organization produces inconsistent outcomes when processing similar cases. Which of the following is the auditor's MOST efficient recommendation?

A. Regularly update the AI algorithm to improve consistency.
B. Introduce a human-in-the-loop mechanism for all decisions.
C. Strengthen user access controls and authorization for the AI system.
D. Analyze the training data and model evaluation parameters.

Answer: D

Explanation:
Inconsistent outcomes for similar cases often indicate issues with "Model Stability" or "Data Quality". The most efficient recommendation is to "Analyze the training data and model evaluation parameters" to identify if the model was trained on biased, noisy, or non-representative datasets.
Inconsistencies may also stem from poorly tuned hyperparameters that cause the model to react erratically to minor variations in input.

NEW QUESTION # 201
In the context of an AI implementation, which of the following actions is MOST critical for an organization's change management program?

A. Conducting a comprehensive risk assessment specific to AI-related changes
B. Reviewing documentation for AI system changes, updates, and patches
C. Verifying that all employees have completed mandatory AI ethics training
D. Ensuring the organization has a dedicated AI governance committee

Answer: A

Explanation:
The AAIA™ Study Guide emphasizes that AI implementations introduce dynamic and non-deterministic elements into systems, increasing the risk associated with changes. A comprehensive, AI-specific risk assessment is therefore the most critical component of a change management program to ensure that updates, retraining, or parameter adjustments do not introduce vulnerabilities or unintended consequences.
"Risk assessments tailored to AI are crucial because changes to models, training data, or infrastructure can affect performance, ethical compliance, or expose the system to new threats. A standard IT change review is often insufficient." While having a governance committee (A) and reviewing documentation (B) are important supporting practices, only option C directly mitigates the core risks of AI system change. Ethics training (D) supports awareness but is not directly tied to change control.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Governance and Risk Management," Subsection: "Change Management and AI Risk Control"

NEW QUESTION # 202
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