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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 (Q263-Q268):

NEW QUESTION # 263
Which control BEST addresses the risk of "model extraction" attacks, where an attacker queries an AI system to reverse-engineer its logic?

Answer: C

Explanation:
Model extraction attacks rely on repeated, systematic querying. Rate limiting and anomaly detection on API usage patterns reduce the attacker's ability to reconstruct the model.


NEW QUESTION # 264
An IS auditor is reviewing a dataset used by a university to train a predictive machine learning model. Which of the following MOST likely indicates risk that the model could not process all data and make necessary correlations?

Answer: A

Explanation:
A numeric field stored as an object (string) format (option C) indicates improper data typing.
Models cannot correctly compute correlations or statistical relationships when numerical values are stored as textual data.
AAIA emphasizes that incorrect datatypes are one of the most common causes of ML model misbehavior, including:
Failure to compute averages, correlations, or mathematical operations
Silent errors in preprocessing
Skewed learning patterns
Incorrect feature importance evaluations
Thus, the Final Grade Percent field in object format is the most significant indicator of processing risk.


NEW QUESTION # 265
Which of the following is the BEST way to mitigate data poisoning in an AI model?

Answer: B

Explanation:
Data poisoning occurs when attackers manipulate training data to corrupt model behavior. The most direct mitigation is to implement robust data validation and integrity checks (option C), including anomaly detection on input distributions, provenance controls, verification of data sources, and safeguards for pipelines feeding the training set. AAIA highlights threats and vulnerabilities specific to AI and the importance of controls that protect data integrity in AI Operations.


NEW QUESTION # 266
To confirm the fairness of AI model decisions, the BEST way to collect reliable evidence during an AI audit is by:

Answer: B


NEW QUESTION # 267
When auditing an AI system, which of the following steps ensures that AI model behavior is aligned with organizational objectives?

Answer: A


NEW QUESTION # 268
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