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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 (Q247-Q252):

NEW QUESTION # 247
An IS auditor notes the combined number of records utilized within the training, validation, and testing data sets exceeds the total number of records in the original data set. Which of the following is MOST important for the auditor to determine?

A. Whether the training, validation, and testing data sets were created in the correct order
B. Whether data leakage occurred from utilizing overlapping records in the data sets
C. Whether a sufficient number of records were utilized in the training data set
D. Whether the validation data set utilized the same number of records as the training data sets

Answer: B

NEW QUESTION # 248
What is the PRIMARY purpose of establishing AI-specific key risk indicators (KRIs), such as drift thresholds or fairness metric thresholds?

A. To provide early, measurable warning signals that trigger investigation or remediation before harm occurs
B. To eliminate the need for human oversight
C. To satisfy marketing requirements
D. To reduce the cost of cloud computing

Answer: A

Explanation:
KRIs translate abstract risks (like drift or bias) into measurable thresholds that trigger proactive action, enabling risk management before issues escalate into incidents.

NEW QUESTION # 249
Which of the following BEST detects model drift or unexpected changes in AI model outputs?

A. AI model retraining
B. AI model documentation reviews
C. Anomaly monitoring
D. Standardization of AI configurations

Answer: C

NEW QUESTION # 250
Which of the following is the GREATEST risk associated with the use of AI coding tools by software developers?

A. Excessive reliance on AI tools to accomplish routine development tasks
B. Difficulty in training developers due to the complexity of AI tools
C. Introduction of security vulnerabilities by AI tools
D. Increased likelihood of human biases in code

Answer: C

Explanation:
AI-based coding assistants can inadvertently generate insecure code patterns, reuse vulnerable libraries, or bypass secure coding practices.
AAIA highlights security vulnerabilities as the primary risk because insecure code can lead to:
* Data breaches
* Injection attacks
* Authentication bypasses
* Supply chain compromise
* Privilege escalation
Excessive reliance (A) affects productivity but not security.
Human bias (B) is possible but not as severe as security flaws.
Training complexity (D) is not a risk to AI system integrity.
Therefore, the introduction of security vulnerabilities is the greatest risk.
References:
AAIA Domain 2: AI Risks in Software Development
AAIA Domain 5: Security and Ethical Development Practices

NEW QUESTION # 251
An IS auditor notes that the combined number of records in the training, validation, and testing sets exceeds the total number of records in the original data set. What is the GREATEST risk?

A. The datasets were created in the incorrect order.
B. The validation dataset is larger than the training set.
C. A sufficient number of records were not used in training.
D. Data leakage occurred from overlapping records.

Answer: D

Explanation:
In a proper AI pipeline, the original data must be " strictly partitioned " into disjoint sets. If the sum of the subsets exceeds the original count, it means some records were " Reused " across sets. This leads to " Data Leakage, " where the model " sees " the test or validation data during the training phase. As a result, the model ' s performance metrics will be artificially inflated, giving a false sense of accuracy. The ISACA AAIA™ Study Guide identifies data leakage as a primary cause of " Model Evaluation Failure, " rendering all testing results invalid.

NEW QUESTION # 252
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