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ISACA AAIA Exam Overview:

Certification Vendor:	ISACA
Exam Name:	ISACA Advanced in AI Audit
Exam Number:	AAIA
Exam Format:	Remotely Proctored, Computer-Based, Multiple Choice
Available Languages:	English
Related Certifications:	CISA ACCA CIA FCCA CPA
Exam Duration:	120 minutes
Passing Score:	65%
Real Exam Qty:	90
Sample Questions:	ISACA AAIA Sample Questions
Exam Way:	Online remotely proctored computer-based exam
Pre Condition:	Candidates must hold an active CISA certification or another qualified audit-related designation such as CIA, CPA, ACCA, FCCA, Canadian CPA, Australian CPA/FCPA, or Japanese CPA.
Official Syllabus URL:	https://www.isaca.org/credentialing/aaia

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ISACA AAIA Exam Syllabus Topics:

Topic	Details
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	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	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.

ISACA Advanced in AI Audit Sample Questions (Q127-Q132):

NEW QUESTION # 127
Which of the following techniques BEST enhances the security and robustness of AI models against adversarial attacks?

A. Input token limitation
B. Defensive distillation
C. User access reviews
D. AI security awareness training

Answer: B

Explanation:
Defensive distillation is a specialized technique designed to make an AI model more robust against adversarial " perturbations " -small changes to input data intended to trick the model. It works by training a smaller model to mimic the probability distributions of a larger, pre-trained model, which effectively " smooths " the decision boundaries and makes it harder for attackers to find exploitable gaps. While access reviews and awareness training are standard security controls, they do not address the mathematical vulnerabilities inherent in ML algorithms. Defensive distillation specifically targets the model ' s resilience to technical exploitation.

NEW QUESTION # 128
Which of the following is the MOST important reason to perform regular ethical reviews of AI systems?

A. To improve the accuracy and performance of the systems
B. To identify and mitigate potential data drift within models
C. To ensure the systems align with the preservation of individual rights
D. To align AI system development with organizational values and principles

Answer: C

NEW QUESTION # 129
An insurance organization deployed an AI tool for assigning customer risk levels. An IS auditor discovers that the learning algorithm is vulnerable to adversarial attacks. Which of the following is the BEST course of action?

A. Validate the process for handling manipulated inputs.
B. Evaluate the number of system logs maintained.
C. Review the speed of learning adaptation.
D. Assess the response time to the attacks.

Answer: A

Explanation:
Adversarial attacks involve " manipulated inputs " (poisoning or evasion) designed to trick a model into making incorrect decisions (e.g., assigning a high-risk driver a low-risk premium). To mitigate this, the auditor must " Validate the process for handling manipulated inputs. " This includes checking for robust input validation, anomaly detection on incoming data, and " adversarial training " where the model is intentionally exposed to these attacks during development to build resilience. Focusing on logs or speed (Options A, B, and C) does not address the fundamental vulnerability of the algorithm to technical manipulation.

NEW QUESTION # 130
What should be the IS auditor's GREATEST concern when an organization is mixing AI- generated content into training data without labeling?

A. The model loses alignment safeguards.
B. The model propagates outdated information.
C. The model outputs become inconsistent across similar queries.
D. The model reinforces its own mistakes as if they were facts.

Answer: D

Explanation:
This is known as "Data Contamination" or "Model Autophagy". When an AI model is trained on data it (or another AI) previously generated, it creates a feedback loop. Errors, hallucinations, and biases in the generated content are re-ingested as "ground truth," which causes the model to
"Collapse" and lose its ability to accurately reflect the real world. The ISACA AAIATM manual emphasizes that training data must be "Primary" and "Authentic" to ensure model integrity. Failing to distinguish between human and AI content prevents the organization from detecting and correcting systemic errors in the model's logic.

NEW QUESTION # 131
Which of the following is the MOST significant benefit of performing frequent AI model testing and retraining?

A. Ensuring the model is updated with current data
B. Removing unnecessary model features
C. Ensuring model scalability
D. Assessing the impact of attacks

Answer: A

Explanation:
AI models are not "static" systems; they are reflections of the data they were trained on. In dynamic environments, data patterns change constantly (e.g., market trends or new fraud tactics). Frequent testing and retraining ensure the model is "updated with current data," which prevents performance degradation and "data drift."

NEW QUESTION # 132
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