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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
  • 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 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 (Q157-Q162):

NEW QUESTION # 157
The PRIMARY purpose of utilizing neural networks in AI is to:

Answer: B


NEW QUESTION # 158
The GREATEST benefit of using AI auditing techniques over traditional methods is that AI auditing techniques can:

Answer: D


NEW QUESTION # 159
An IS auditor detected a " Prompt Injection " embedded in an email from a vendor that used an invisible font to hide text. Which of the following is the BEST control?

Answer: D

Explanation:
This is a " Hidden Text " attack, where an attacker tricks an LLM by embedding instructions that the human reader cannot see but the machine can process. The most effective " Incident Management " control is " Text Sanitization " that specifically strips out invisible formatting, hidden HTML tags, or zero-width characters before the text is sent to the AI. Adding instructions (Option B) is unreliable because prompt injections are specifically designed to " override " previous instructions. Lowering the temperature (Option A) reduces creativity but doesn ' t stop the model from following a clear, albeit hidden, command.


NEW QUESTION # 160
Which of the following should be an IS auditor's GREATEST concern when reviewing an anomaly detection process implemented for a high-risk AI system?

Answer: A

Explanation:
In a high-risk AI system, anomaly detection often serves as a frontline control to flag irregularities in input data and model behavior. The GREATEST concern for an IS auditor is when the process fails to identify anomalies that can bias training data (A), because undetected anomalies can fundamentally distort model learning and outputs. This can lead to systemic bias, incorrect decisions, safety risks, and regulatory breaches.


NEW QUESTION # 161
Which of the following types of AI can use unlabeled data sets to imitate human learning behavior?

Answer: C

Explanation:
Unsupervised learningusesunlabeled datato discover patterns, structures, or groupings without explicit outcome labels. The model "learns" by identifying similarities, clusters, or latent structures within the data, somewhat analogous to how humans can notice patterns without being told the correct answer. In AAIA's fundamentals coverage, unsupervised methods (e.g., clustering, dimensionality reduction) are explicitly linked to situations wherelabels are unavailable or costly, yet insight is still needed.
Supervised learning (A) requires labeled examples (input-output pairs). Federated learning (B) describes a distributed training paradigm, not the labeling requirement itself. Reinforcement learning (C) uses feedback in the form of rewards and penalties rather than unlabeled static datasets. Therefore,unsupervised learningis the correct type that directly usesunlabeled datato learn structure.
References:
ISACA,AAIA Exam Content Outline- Domain 1: AI Models, Considerations, and Requirements (supervised, unsupervised, reinforcement learning).
ISACA AI fundamentals content on learning types and data labeling.


NEW QUESTION # 162
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