100% Pass Quiz 2026 Newest ISACA AAIA: Test ISACA Advanced in AI Audit Pdf

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ISACA AAIA Exam Syllabus Topics:

SectionObjectives
AI Operations
AI Governance and Risk
AI Auditing Tools and Techniques

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ISACA Advanced in AI Audit Sample Questions (Q217-Q222):

NEW QUESTION # 217
Which of the following represents the PRIMARY benefit of reviewing model cards during AI model acquisition and risk assessment?

Answer: C

Explanation:
A "Model Card" is a standardized document that provides essential information about an AI model's intended use, training data, limitations, and performance metrics. For an auditor or risk manager, the primary benefit is gaining a clear "Understanding of model intent and performance context." It allows the organization to determine if a vendor's model is fit for the specific business purpose and to identify potential risks (such as data bias or environmental limitations) before acquisition.


NEW QUESTION # 218
Which of the following is the PRIMARY objective of performing adversarial testing on AI models?

Answer: B

Explanation:
Adversarial testing involves simulating real-world attacks or malicious inputs against AI models (e.g., adversarial examples, poisoning, evasion) to identify how the system behaves under intentional misuse or hostile conditions. The primary objective is to discover weaknesses and control gaps (D) in the model and its surrounding processes--such as inadequate input validation, insufficient monitoring, or missing safeguards against adversarial inputs.


NEW QUESTION # 219
An IS auditor uses an internally developed generative AI tool to prepare a status update for audit stakeholders. Which of the following is the auditor's MOST appropriate course of action?

Answer: A


NEW QUESTION # 220
The PRIMARY benefit of using AI in audit fieldwork is that it:

Answer: C

Explanation:
According to the AAIATM manual, the transformative power of AI in audit fieldwork lies in its ability to process 100% of a data population at scale. While traditional audit methods rely on manual sampling, which may miss infrequent or complex outliers, AI-driven tools can efficiently scan massive, multi-dimensional datasets to pinpoint anomalies and emerging trends that warrant further investigation. This increases the depth of the audit and allows for a more rigorous risk- based approach.


NEW QUESTION # 221
An organization deploys an AI-based image recognition system that is vulnerable to evasion attacks. Which of the following approaches BEST helps to ensure the system mitigates these evasion attempts?

Answer: B

Explanation:
Evasion attacks occur when an attacker modifies input data (such as adding subtle noise to an image) to trick a model into misclassification. The AAIA™ manual identifies " Adversarial Training " as a primary defense, where the model is intentionally exposed to adversarial examples during the training phase to improve its robustness and resilience. This allows the model to learn the patterns associated with malicious inputs. While static filtering (Option A) and ensembles (Option C) can provide layers of defense, they are often bypassed by sophisticated attacks. Regular bias reviews further ensure that the model's decision-making remains fair and consistent across all inputs, including those designed to exploit algorithmic weaknesses.


NEW QUESTION # 222
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