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ISACA AAIA Exam Syllabus Topics:

TopicDetails
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 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 3
  • 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.

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ISACA Advanced in AI Audit Sample Questions (Q96-Q101):

NEW QUESTION # 96
An IS auditor analyzed an AI model scorecard and identified that training data was imbalanced.
Which of the following is the BEST recommendation to remediate risk?

Answer: D

Explanation:
Data imbalance (where one class significantly outweighs another) leads to biased models that perform poorly on minority classes. The ISACA AAIATM manual suggests that "class-weighted loss" is a proactive technical control that penalizes the model more for misclassifying minority samples, effectively "balancing" the learning process. Stratified splitting ensures that every training and validation set maintains the same ratio of classes as the original data, preventing the model from failing to "see" minority cases during the testing phase.


NEW QUESTION # 97
Which of the following techniques would be MOST effective as part of incident management procedures for a prompt injection attack?

Answer: D

Explanation:
Prompt injection attacksinvolve maliciously crafted inputs intended to override system instructions, exfiltrate data, or cause harmful behavior. The most effective control aligned with incident management is to deploy robust input validation and sanitization(C), which includes rules and filters designed to detect and neutralize potentially malicious content before it reaches the model. AAIA's coverage of AI threats and vulnerabilities highlights the importance ofinput validation and secure prompt handlingfor generative AI systems.
Fine-tuning the model (A) is a long-term adaptation, not an immediate incident control. Scanning for code- like structure (B) or excessive special characters (D) may catch some attacks but are too narrow; many prompt injections use natural language. Comprehensiveinput validationand sanitization is the most effective and generalizable incident management response.
References:
ISACA,AAIA Exam Content Outline- Domain 5: Ethical and Legal Considerations in AI; AI-specific threats and incident management.
ISACA AI security guidance covering prompt injection and input validation controls.


NEW QUESTION # 98
Which of the following techniques is BEST to use when there is a limited dataset of detailed images available to train a convolutional neural network (CNN) model?

Answer: C

Explanation:
When data is scarce, training a deep CNN from scratch is likely to result in overfitting. "Transfer learning" allows an organization to take a model previously trained on a massive dataset (like ImageNet) and "fine-tune" it for their specific, smaller dataset. The model already knows how to recognize basic shapes and textures, so it requires much less data to learn specific new categories. This is a common and highly effective practice in medical imaging or specialized manufacturing audits.


NEW QUESTION # 99
An organization wants to reduce the risk of over-reliance ("automation bias") among employees using an AI decision-support tool. Which control is MOST effective?

Answer: A

Explanation:
Automation bias -- the tendency to over-trust automated outputs -- is best mitigated through training that encourages critical evaluation, combined with transparency features (confidence scores, rationale) that support informed human judgment.


NEW QUESTION # 100
The BEST way to prevent sensitive information disclosure by large language model (LLM) chatbots is through:

Answer: C

Explanation:
Large Language Models (LLMs) have the capacity to memorize and unintentionally reveal sensitive information if not properly managed. As per the AAIATM Study Guide, data masking is a critical technique that prevents the exposure of personally identifiable information (PII) or confidential content by obscuring or replacing sensitive parts of the data during training or interaction.
"Data masking ensures that training data used for LLMs does not contain real sensitive identifiers. Unlike sanitization, masking modifies data to maintain utility while eliminating exposure risk." Manual monitoring and access controls are supportive security measures, and data sanitization helps remove content but may not preserve the data's structure. Data masking offers the most proactive and technically robust solution.


NEW QUESTION # 101
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