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ISACA AAIA Exam Syllabus Topics:

Topic	Details
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 (Q101-Q106):

NEW QUESTION # 101
A healthcare organization uses an AI model to analyze patient data and provide diagnostic recommendations. Which of the following MOST effectively detects data drift related to the model's predictions?

A. Conducting periodic model retraining to ensure alignment with updated patient data
B. Using adversarial testing to simulate scenarios that stress test the model's predictions
C. Applying overrides to allow healthcare professionals to correct the AI model's recommendations
D. Comparing incoming patient data distributions with the training data set

Answer: D

Explanation:
Detecting data drift is critical in maintaining the reliability and accuracy of AI models, especially in dynamic environments like healthcare where patient populations and data characteristics can change over time. According to the ISACA Advanced in AI AuditTM (AAIATM) Study Guide, data drift refers to changes in the input data's statistical properties compared to the data on which the model was originally trained. If not detected, data drift can degrade model performance and lead to erroneous predictions.
The most effective approach to detect data drift is to continuously compare the statistical distributions of incoming (production) data with those of the training data set. This allows organizations to identify deviations in data patterns, which can be early indicators that the AI model's predictions may no longer be valid or optimal.
As stated in the AAIATM Study Guide under "AI Model Monitoring and Maintenance":
"Monitoring input data for distributional changes compared to the model's training data is an essential step in identifying data drift. Statistical tests and visualizations can help auditors and AI operators detect when the underlying data characteristics have shifted, prompting further investigation or retraining needs."

NEW QUESTION # 102
What is the MAIN audit risk of "shadow AI" (AI tools adopted by business units without IT/security approval)?

A. Lack of visibility, governance, and risk controls over unsanctioned AI use
B. Increased employee productivity
C. Higher subscription costs
D. Faster deployment cycles

Answer: A

Explanation:
Shadow AI bypasses formal risk assessment, data governance, and security review, creating blind spots for data leakage, compliance violations, and unmanaged model risk.

NEW QUESTION # 103
Which of the following controls MOST effectively helps to ensure an AI model is resilient against external threats?

A. Monitoring of AI model developers
B. Monitoring of AI access logs
C. AI model configuration testing
D. AI data set anonymization

Answer: C

Explanation:
Ensuring AI model resilience against external threats involves validating that the model is configured to resist attacks, such as adversarial inputs, data poisoning, or misuse. The AAIATM Study Guide emphasizes configuration testing as a crucial control to simulate threat scenarios and assess robustness.
"Model configuration testing simulates real-world threat conditions to validate model resilience.
This includes testing against adversarial attacks, input manipulation, and exposure of sensitive outputs." While access monitoring (C) and anonymization (A) reduce risks, they don't actively validate model behavior under threat conditions. Therefore, D offers the most effective resilience measure.

NEW QUESTION # 104
Which of the following is the MOST important course of action for an organization prior to allowing end users to utilize an AI tool?

A. Ensure a cybersecurity insurance clause is in place to include the use of AI.
B. Implement baseline performance metrics.
C. Determine the impact to the disaster recovery plan (DRP).
D. Develop an AI policy with guidelines on appropriate use.

Answer: D

NEW QUESTION # 105
Which of the following insider threats involving the use of AI would present the GREATEST risk?

A. Exfiltrating sensitive data
B. Leaking of system hyperparameters
C. Launching social engineering attacks
D. Destroying system backups

Answer: A

Explanation:
The GREATEST insider threat is exfiltrating sensitive data (D). AI systems often contain rich datasets including personal, financial, operational, and proprietary information. If an insider extracts or leaks this data, the result can be severe legal, regulatory, and reputational consequences.

NEW QUESTION # 106
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