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ISACA AAIA Exam Syllabus Topics:

TopicDetails
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 (Q277-Q282):

NEW QUESTION # 277
Which of the following is the MOST important task when gathering data during the AI system development process?

Answer: D

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 # 278
An IS auditor identifies that an AI model occasionally invents nonexistent medical test results.
Which of the following recommendations would BEST mitigate this risk?

Answer: B

Explanation:
Inventing nonexistent medical test results is a form of hallucination commonly associated with generative AI models. Reducing top-p sampling (A) restricts the model to choosing from the most probable next tokens, reducing randomness and preventing the generation of fabricated or implausible content. AAIA emphasizes configuration tuning (temperature, top-k, top-p) as critical controls for mitigating hallucinations.


NEW QUESTION # 279
A bank uses a video-based know your customer (KYC) verification process. Cybercriminals exploit this process by using deepfake technology to impersonate bank customers. Which of the following countermeasures is the BEST way for the bank to mitigate this risk?

Answer: A

Explanation:
Liveness detection is the most effective countermeasure against deepfakes in video-based verification. AI- based liveness detection analyzes facial movement, micro-expressions, and other biometric cues to differentiate real humans from manipulated video content.
"To protect against identity spoofing and deepfake exploitation, biometric systems must incorporate liveness detection protocols capable of detecting synthetic imagery or falsified video data." Encryption (C) protects data at rest and in transit but does not prevent impersonation. Discontinuation (D) may not be necessary if effective countermeasures like B are in place.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Governance and Risk Management," Subsection: "Biometric Security and AI Authentication Methods"


NEW QUESTION # 280
Which control BEST mitigates the risk of a machine learning model being manipulated through adversarial input perturbations?

Answer: C

Explanation:
Adversarial robustness testing deliberately crafts inputs designed to fool the model, allowing teams to measure and harden against evasion attacks before deployment.


NEW QUESTION # 281
An IS auditor is reviewing AI-driven processes that automate financial approvals. Which of the following poses the GREATEST challenge to maintaining separation of duties (SOD)?

Answer: B

Explanation:
Separation of Duties (SOD) is intended to prevent errors or fraud by ensuring no single person (or entity) has control over all stages of a transaction. When AI automates financial approvals, the " Lack of transparency in the algorithm " creates a challenge because the logic that grants approval is often opaque. If the algorithm functions as a " Black Box, " it may consolidate decision-making power that was previously spread across multiple human roles, without sufficient visibility into how those decisions are made. This can lead to unauthorized or erroneous approvals going undetected. Effective AI governance must ensure that automated approvals are subject to transparent audit trails and secondary human reviews to uphold SOD principles.


NEW QUESTION # 282
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