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ISACA AAIA Exam Syllabus Topics:

Topic	Details
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	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 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 (Q101-Q106):

NEW QUESTION # 101
A digital bank utilizes an AI system to generate credit scores. Which of the following would BEST mitigate the risk of sudden and unexplained changes in a borrower's credit score?

A. Ensuring the system is periodically reviewed and calibrated by human experts to maintain stability in predictions
B. Allowing the AI to operate fully autonomously to prevent processing delays
C. Using only data from the last six months to one year to avoid outdated information affecting the credit score
D. Obtaining and validating the credit scores from third-party agencies to cross-check AI-generated results

Answer: A

Explanation:
Sudden and unexplained changes in AI-generated credit scores may result from data drift, model overfitting, or lack of recalibration. According to the AAIATM Study Guide, regular expert review and calibration help maintain model reliability and transparency, particularly in high-stakes decisions like credit scoring.
"Ongoing human oversight ensures that predictive models remain stable and justifiable. In high- impact environments, such as banking, experts must review and recalibrate AI systems to prevent opaque or unexpected behavior."

NEW QUESTION # 102
An IS auditor is evaluating a large language model (LLM) before deployment. Which of the following is the MOST secure way to manage agency for the model?

A. Rely on LLMs to automatically manage authorization and privilege checks.
B. Ensure the LLM is trained on adversarial datasets.
C. Ensure authorization and privilege checks are performed independently of the LLM.
D. Use LLMs to manage data feeds and sources.

Answer: C

Explanation:
"Agency" refers to the model's ability to take actions or access data. LLMs are non-deterministic and can be tricked via "prompt injection" to ignore their internal rules. Therefore, "Authorization and privilege checks" must be performed by a separate, deterministic security layer that is
"independent of the LLM." According to the ISACA AAIATM Study Guide, you should never allow an AI to decide its own permissions (Option D) or those of other systems. If a user asks an AI to delete a file, the AI should simply "request" the deletion, and a standard, non-AI security system should check if the user has the right to do so. This maintains the "Principle of Least Privilege" and prevents unauthorized actions via model manipulation.

NEW QUESTION # 103
An organization is conducting an audit of an AI decision-making system being used for talent recruitment. Which of the following is MOST critical to evaluate in order to ensure the system meets stakeholder needs?

A. Predictive accuracy
B. Decision fairness
C. Input validation
D. Decision timeliness

Answer: B

Explanation:
While predictive accuracy and timeliness are operational requirements, "Decision fairness" is the most critical ethical and legal consideration in recruitment AI. Stakeholders, including candidates, regulators, and management, require assurance that the system does not discriminate against protected groups based on gender, age, or ethnicity. The ISACA AAIATM Study Guide emphasizes that for high-impact human resource applications, auditors must prioritize fairness testing and bias assessments to prevent reputational damage and legal liability. A system that is highly accurate but biased fails to meet the ethical standards and "stakeholder needs" of modern governance.

NEW QUESTION # 104
In the context of an AI implementation, which of the following actions is MOST critical for an organization's change management program?

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

Answer: C

Explanation:
The AAIATM 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."

NEW QUESTION # 105
A bank ' s fraud detection model achieves high accuracy on its initial dataset but performs poorly in production. The data science team needs to tune hyperparameters and select the best model architecture.
Which dataset is BEST to use for this selection process?

A. Validation dataset
B. Training dataset
C. Testing dataset
D. Holdout dataset

Answer: A

Explanation:
In the standard machine learning workflow, the " Validation Dataset " is specifically used for " model selection " and " hyperparameter tuning. " It serves as a bridge between training and the final test. Using the training set (Option B) for tuning would lead to overfitting. The " Testing " or " Holdout " set (Options A and C) must remain completely " unseen " until the very end to provide an unbiased final estimate of how the model will perform in the real world. According to the ISACA AAIA™ manual, maintaining this strict partitioning is critical for model integrity and preventing overly optimistic performance reports.

NEW QUESTION # 106
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