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ISACA AAIA Exam Syllabus Topics:

SectionWeightObjectives
AI Governance and Risk33%- Ethics, Regulations, and Standards for AI
- AI Governance and Program Management
- Privacy and Data Governance Programs
- AI Models, Considerations, and Requirements
- AI Risk Management
AI Auditing Tools and Techniques21%- Audit Evidence Collection Techniques
- Audit Testing and Sampling Methodologies
- AI Audit Outputs and Reporting
- Data Quality and Analytics for AI Audit
- Audit Planning and Design
AI Operations46%- Operational Controls and Readiness
- AI System Lifecycle and Deployment
- Performance Monitoring and Evaluation
- Incident Management and Resilience
- Third-Party and Supply Chain Risk

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ISACA Advanced in AI Audit Sample Questions (Q61-Q66):

NEW QUESTION # 61
An IS auditor notes that an AI modelachieved significantly better results on training data than on test data. After the development, a quality assurance (QA) team checks that all input variables and parameters match the technical design. Which of the following BEST describes this activity?

Answer: C

Explanation:
The ISACA AAIATM framework distinguishes between "Verification" and "Validation".
"Verification" is a QA process that checks if the system was "built right"--i.e., whether the code, parameters, and architecture match the design documentation.


NEW QUESTION # 62
Which of the following is the PRIMARY objective of AI governance?

Answer: D

Explanation:
The AAIATM Study Guide defines the primary objective of AI governance as establishing structure and accountability for AI initiatives. This includes clearly assigning responsibilities across development, deployment, risk management, and auditing roles to ensure that AI is used responsibly and transparently.
"AI governance establishes the policies, roles, and oversight structures that guide the ethical and secure deployment of AI. Clear accountability helps prevent unauthorized use and ensures strategic alignment."


NEW QUESTION # 63
Which of the following is the MOST important task when gathering data during the AI system development process?

Answer: B

Explanation:
Data cleaning is a foundational task in the AI development lifecycle. The AAIA™ Study Guide identifies data quality-ensuring completeness, accuracy, consistency, and correctness-as critical to building effective and unbiased AI systems. Cleaning the data involves removing duplicates, correcting errors, addressing missing values, and standardizing formats.
"Data cleaning is a prerequisite for effective training and evaluation. Poor-quality data leads to inaccurate or misleading model outputs, increasing operational and ethical risks." While training (D) is essential, it must occur only after the data has been adequately prepared. Stratification (A) supports certain modeling approaches but is secondary to data integrity. Therefore, C is the most important task at the data-gathering stage.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Fundamentals and Technologies," Subsection: "Data Collection and Preparation"


NEW QUESTION # 64
Which of the following is the MOST important reason to perform regular ethical reviews of AI systems?

Answer: C


NEW QUESTION # 65
Which of the following types of AI can use unlabeled data sets to imitate human learning behavior?

Answer: D

Explanation:
Unsupervised learning uses unlabeled data to discover patterns, structures, or groupings without explicit outcome labels. The model "learns" by identifying similarities, clusters, or latent structures within the data, somewhat analogous to how humans can notice patterns without being told the correct answer. In AAIA's fundamentals coverage, unsupervised methods (e.g., clustering, dimensionality reduction) are explicitly linked to situations where labels are unavailable or costly, yet insight is still needed.


NEW QUESTION # 66
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