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ISACA AAIA Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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 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.

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ISACA Advanced in AI Audit Sample Questions (Q317-Q322):

NEW QUESTION # 317
A manufacturing company installs an AI system to control robotic arms on its assembly line. The system learns over time, adjusting its movements based on production results to improve accuracy. What type of learning is the robot MOST likely using?

Answer: C

Explanation:
Reinforcement Learning (RL) is characterized by an " agent " (the robot) that learns to achieve a goal in a dynamic environment through trial and error. It receives " rewards " for correct movements and " penalties " for errors, allowing it to optimize its " Policy Function " over time. Unlike supervised learning (Option A), which requires a pre-labeled dataset, RL allows for continuous improvement based on real-time feedback. The ISACA AAIA™ Study Guide identifies RL as the standard for robotics and autonomous systems where the " correct " action depends on the state of the physical environment.


NEW QUESTION # 318
Which of the following controls MOST effectively helps to ensure an AI model is resilient against external threats?

Answer: C


NEW QUESTION # 319
An AI-based marketing analytics tool is trained on data that is five years old. Which of the following is MOST likely to occur?

Answer: B

Explanation:
Model drift (also called concept drift) occurs when the data used for training no longer reflects the current environment. In marketing, consumer behaviors, trends, and demographics change rapidly; therefore, a model trained on five-year-old data will suffer from significant drift. The AAIATM manual explains that this results in the model making predictions based on obsolete patterns, leading to poor accuracy and potentially biased or irrelevant marketing recommendations.


NEW QUESTION # 320
Which of the following would provide the BEST evidence to an IS auditor that an AI model's outputs are effectively controlled for bias?

Answer: B

Explanation:
To determine whether an AI model's outputs areeffectively controlled for bias, an auditor needsempirical performance evidenceacross demographic groups.Similar accuracy ranges across groups (A)demonstrate that the model performs equitably and does not disproportionately advantage or disadvantage specific populations. This aligns with AAIA's emphasis onfairness metrics, such as disparate error rates, equal opportunity, and demographic parity analyses.
Option B (fairness definition) is important for governance but does not prove fairness in practice. Option C may introduce historical human bias rather than mitigate it. Option D (transparency of development) supports explainability but does not validate fairness outcomes. Actualperformance parity across demographic groupsis the strongest evidence of bias control.
References:
ISACA,AAIA Exam Content Outline- Domain 1: AI Governance and Risk (fairness evaluation; bias assessments).
ISACA fairness guidance emphasizing cross-group performance comparison.


NEW QUESTION # 321
Which of the following should be applied to an AI system but are not typically used in traditional systems?

Answer: B

Explanation:
AI systems faceunique threatsnot commonly found in traditional IT environments, particularlydata poisoning, where attackers manipulate training data to corrupt model behavior. Controls that specifically monitor and mitigate poisoning-such as input provenance checks, anomaly detection on training data, and integrity validation pipelines-are emphasized in AAIA's coverage ofAI-specific vulnerabilities.
While privacy (A), data exfiltration (C), and data governance (D) controls are essential for all digital systems, monitoring for data poisoningis uniquely critical for AI because poisoned inputs can lead to faulty predictions, safety issues, or systemic bias. AAIA specifically highlights data poisoning as a distinct threat requiring specialized controls.
References:
ISACA,AAIA Exam Content Outline- Domain 2: Threats and Vulnerabilities Specific to AI.
ISACA AI security guidance discussing poisoning and integrity attacks.


NEW QUESTION # 322
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