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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	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 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 (Q199-Q204):

NEW QUESTION # 199
A healthcare organization uses patient data to train an AI model for early disease detection. Which of the following practices provides the BEST assurance that personal data is secure and its integrity is maintained?

A. Updating the AI model with new data and tracking changes
B. Encrypting stored data to reduce exposure and log access
C. Anonymizing patient data and performing regular quality checks
D. Implementing strict data access controls and conducting security tests

Answer: C

NEW QUESTION # 200
During an audit of an AI incident response plan, what is the MOST important element specific to AI (beyond a standard IT incident plan)?

A. A post-incident report template
B. A designated incident commander
C. Procedures for identifying and containing model-specific failures such as drift, bias incidents, or data poisoning
D. A communication plan template

Answer: C

Explanation:
Generic IT incident plans do not typically address AI-specific failure modes; a mature plan must explicitly define detection and containment procedures for issues like drift, poisoning, or biased outputs.

NEW QUESTION # 201
Which of the following is the MOST important reason to establish AI governance structures that extend beyond regulatory compliance?

A. To align with global AI data privacy standards
B. To ensure ethical integrity throughout the AI life cycle
C. To mitigate reputational risk associated with public scrutiny of AI systems
D. To establish guardrails limiting AI system functionality to approved use cases

Answer: B

Explanation:
While regulatory compliance is essential, AAIA underlines thatethical integritymust guide AI design, deployment, and monitoring. Regulations often lag behind technological capabilities; thus, relying solely on compliance leaves gaps in areas such as fairness, transparency, human dignity, and societal impact. The MOST important reason to extend governance structures beyond compliance is toensure ethical integrity throughout the AI life cycle(C) - from data collection and model design to deployment, monitoring, and retirement.
Option A focuses on privacy only, a subset of broader ethical considerations. Option B is valid but secondary; reputational protection is often a consequence of doing the right thing ethically. Option D (guardrails) is part of governance, but the overarching rationale for those guardrails is to uphold ethical principles. Therefore, comprehensive ethical stewardshipis the key driver.
References:
ISACA,AAIA Exam Content Outline- Domain 5: Ethical and Legal Considerations in AI (ethical AI principles, beyond compliance).
ISACA AI ethics and governance guidance emphasizing proactive, values-driven AI oversight.

NEW QUESTION # 202
An IS auditor is reviewing change management documentation of an AI model. Which of the following would pose the GREATEST risk to the model?

A. Models were tuned on a training set
B. An A/B test comparing models using different seeds
C. An A/B test comparing models using the same seed
D. Models were trained on the same data splits

Answer: B

Explanation:
In AI development, a "seed" ensures that random processes (like weight initialization) are reproducible. If an A/B test compares two models using different seeds, the auditor cannot tell if the performance difference is due to the model changes or simply due to "random luck" in how the weights were initialized. This invalidates the test results. For a fair "apple-to-apples" comparison, the seed should remain consistent. Tuning on a training set (Option B) is standard, though it risks overfitting; however, the lack of scientific control in testing (Option C) is a more immediate risk to the integrity of the change management process.

NEW QUESTION # 203
Which of the following AI documents would support an IS auditor assessing hyperparameter tuning records?

A. Data sheets
B. Risk assessment reports
C. Model development logs
D. Explainability reports

Answer: C

Explanation:
Hyperparameter tuning involves adjusting settings like learning rate, batch size, or layer depth to optimize performance. This process is documented in "Model development logs," which record the different "experiments" run by data scientists, the parameters used in each, and the resulting performance metrics (e.g., accuracy or loss). For an auditor, these logs are the "Audit Trail" of the model's construction.

NEW QUESTION # 204
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