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ISACA AAIA Exam Syllabus Topics:

Topic
Details

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 (Q25-Q30):

NEW QUESTION # 25
An organization is evaluating change management practices for AI-based decision support models. Which of the following BEST demonstrates effective AI-focused change management?

A. Assigning a single data science team member to adjust the model in order to establish accountability
B. Engaging an independent expert to review the model's accuracy and precision on a quarterly basis
C. Deploying two separate copies of the model after each adjustment to compare results
D. Documenting model updates and retraining sessions to ensure traceability

Answer: D

Explanation:
Documenting model updates and retraining sessions to ensure traceability (option C) is the hallmark of mature, audit-ready change management in AI. The AAIA™ Study Guide states, "Traceability and documentation of all changes-including retraining events, parameter adjustments, and update rationales- enable effective audit trails, accountability, and regulatory compliance for AI systems." While reviews and comparisons are useful, only comprehensive documentation guarantees ongoing transparency and effective management.
Reference:ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Change Management and Traceability in AI"

NEW QUESTION # 26
An organization is using information gathered from customer accounts to train its AI chatbot. Which of the following is the GREATEST risk associated with this practice?

A. Transparency
B. Disclosure of personal information
C. AI model hallucinations
D. AI bias

Answer: B

NEW QUESTION # 27
An IS auditor is performing an inventory audit for a manufacturing organization. Which of the following would BEST enable the auditor to identify types of products without assistance from organizational staff?

A. Robotic process automation (RPA)
B. Natural language processing
C. Speech modeling
D. Computer vision

Answer: D

NEW QUESTION # 28
Which of the following is MOST important for an IS auditor to consider when identifying AI risk in a know your customer (KYC) application within a banking organization?

A. Benchmarking against peer organizations
B. Business disruption and financial impact
C. Incident response plan
D. Intellectual property leakage and invalidation

Answer: B

Explanation:
In high-stakes financial applications like KYC, the primary concern is the potential business and regulatory impact of an AI error-such as false customer rejection or failure to detect fraudulent accounts. The AAIA™ Study Guide emphasizes aligning AI risk assessments with business impact and regulatory exposure.
"In financial institutions, the most material risk of AI errors lies in operational disruption and regulatory fines.
KYC models must be assessed for how errors can lead to compliance failures or reputational harm." Benchmarking (B) supports best practice alignment, and incident response (C) is part of mitigation, but D addresses the most critical consequence of AI risks in banking.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Governance and Risk Management," Subsection: "Risk Impact and Business Alignment in AI Systems"

NEW QUESTION # 29
Which of the following techniques would be MOST effective as part of incident management procedures for a prompt injection attack?

A. Monitor the prompts for excessive special characters.
B. Deploy input validation to sanitize abuse prompts.
C. Scan inputs for code-like structure of text.
D. Fine-tune the AI model.

Answer: B

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 # 30
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