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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.

ISACA Advanced in AI Audit Sample Questions (Q62-Q67):

NEW QUESTION # 62
Which of the following is MOST effective in analyzing unlabeled datasets to identify anomalies?

A. Isolation forest
B. Supervised learning
C. Principal component analysis
D. Z-score analysis

Answer: A

Explanation:
Isolation Forest is specifically designed foranomaly detectionin unlabeled datasets. It works by isolating observations through random partitioning, making it highly effective for identifying rare, unusual, or suspicious data points without requiring labeled examples.
AAIA emphasizes using unsupervised anomaly detection techniques for scenarios involving:
* Fraud detection
* Network intrusion identification
* Operational anomaly analysisPCA (B) reduces dimensionality but is not an anomaly detector. Z-score (C) assumes normal distributions and is less effective for complex datasets. Supervised learning (D) requires labels, making it unsuitable for unlabeled anomaly detection.Isolation Forest is the most aligned with AAIA's unsupervised anomaly detection standards.
References:
AAIA Domain 1: AI Models and Learning Types.
AAIA Domain 2: Unsupervised Techniques for Anomaly Detection.

NEW QUESTION # 63
Which of the following is the PRIMARY reason IS auditors must be aware that generative AI may return different investment recommendations from the same set of data?

A. Neural node access varies each time the process is executed.
B. Servers are reconfigured periodically.
C. Computational logic is based on probabilities.
D. Limitations can arise in the quantification of risk profiles.

Answer: C

NEW QUESTION # 64
When using off-the-shelf AI models, which of the following is the MOST appropriate way for organizations to approach vendor management?

A. Use the vendor only if the contract has been reviewed by the information security department.
B. Only use models from vendors with globally recognized accreditation.
C. Ensure a minimum of three quotes have been obtained for market research and comparison.
D. Establish responsibility and clear terms for model updates and support.

Answer: D

NEW QUESTION # 65
The PRIMARY purpose of utilizing neural networks in AI is to:

A. Increase computational power.
B. Improve the user interface.
C. Mimic human decision making.
D. Minimize maintenance costs.

Answer: C

NEW QUESTION # 66
Which of the following is the BEST way to support the development and design of high-risk AI systems?

A. Conduct regular training sessions for users on data privacy.
B. Regularly back up the AI system's data to a secure, offsite location.
C. Ensure the availability of trustworthy data sets.
D. Implement multi-factor authentication (MFA) for all users accessing the AI system.

Answer: C

Explanation:
The AAIA™ Study Guide emphasizes that the foundation of any high-performing and ethical AI system lies in the quality and integrity of its data. For high-risk AI systems, such as those used in healthcare, finance, or criminal justice, it is essential to base models on trustworthy data. This ensures reliable predictions, reduces bias, and mitigates risk.
"Trustworthy datasets are characterized by accuracy, completeness, consistency, and ethical sourcing. In high- risk AI applications, ensuring data quality at every stage is crucial to system reliability and compliance." While backups, user training, and MFA are important for security and operational resilience, they do not address the core challenge of ensuring model accuracy and fairness at the development and design phase.
Therefore, option C is the most effective practice.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Fundamentals and Technologies," Subsection: "Data Governance and Management"

NEW QUESTION # 67
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