Exam AAIA Guide & Exam AAIA Answers

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ISACA AAIA Exam Syllabus Topics:

Topic	Details
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	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 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.

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ISACA Advanced in AI Audit Sample Questions (Q118-Q123):

NEW QUESTION # 118
Which of the following is the BEST recommendation for an organization that has adopted "vibe coding" (using AI to generate code based on high-level natural language prompts)?

A. Discontinue the current practice.
B. Adopt a security checklist for code review.
C. Build the organization's own cryptography.
D. Prompt the AI to review its own code.

Answer: B

Explanation:
"Vibe coding" or AI-assisted development carries the risk of introducing "hallucinated" vulnerabilities or insecure coding patterns that the AI learned from public (and potentially flawed) repositories. The most responsible control is to implement a rigorous, human-led "security checklist" for code reviews. This ensures that every line of AI-generated code is checked for common flaws like SQL injection or hardcoded credentials.

NEW QUESTION # 119
An IS auditor is planning an audit of an AI medical prognosis system. Which of the following is the BEST way to test model transparency?

A. Use the SHapley Additive exPlanations (SHAP) method
B. Build clinical integration workflows
C. Document diagnostic tools for stress tests
D. Design output assessments for recognized healthcare standards

Answer: A

Explanation:
SHAP (Shapley Additive Explanations) is the leading, industry-recognized method for explaining complex AI model predictions.
AAIA specifically identifies SHAP as a robust technique for transparency in high-impact systems such as medical AI.
SHAP provides:
* Feature contribution breakdowns
* Visual explanation of each decision
* Global and local interpretability
* Quantifiable insights into model logic
* Compliance-friendly justification of predictions
Options A and B relate to integration and stress testing, not transparency.
Option C validates outputs but does not explain how the model reached them.
Thus, SHAP is the best approach for achieving explainability in medical AI.
References:
AAIA Domain 5: Explainability Techniques
AAIA Domain 3: Model Validation and Auditability

NEW QUESTION # 120
When an IS auditor reviews audit evidence gathered by AI, which of the following practices should the auditor perform to ensure the reliability of the evidence?

A. Validate the evidence against source documents.
B. Interview the organizational management team.
C. Review the AI model card.
D. Evaluate the completeness of the evidence collection procedure.

Answer: A

Explanation:
Professional skepticism is a core requirement for auditors using AI tools. Because AI can suffer from " hallucinations " or misinterpretation of data, the auditor must " Validate the evidence against source documents. " This involves taking a sample of the AI ' s findings and manually verifying them against the original records (e.g., invoices, contracts, or logs). According to the ISACA AAIA™ Study Guide, while reviewing model cards (Option A) provides context on the tool ' s capabilities, it does not confirm that a specific piece of evidence is accurate. Direct verification against the " Ground Truth " (source documents) is the only way to ensure the reliability and integrity of AI-generated audit evidence.

NEW QUESTION # 121
Which of the following BEST describes the auditor's role when reviewing an organization's AI governance maturity?

A. Approving all new AI projects before development begins
B. Managing the organization's data science team
C. Building the AI models being audited
D. Independently assessing whether governance structures, policies, and controls are designed and operating effectively across the AI lifecycle

Answer: D

Explanation:
The auditor's role is independent assurance -- evaluating whether governance, risk, and control structures exist and function effectively -- not operational involvement in building or approving AI systems.

NEW QUESTION # 122
Which of the following is MOST important for an IS auditor to consider when collecting data for analysis by AI tools?

A. Location of and access restrictions to the data
B. Data classification categories
C. Model weights used for AI training
D. Data format and syntax requirements

Answer: D

Explanation:
When collecting data for analysis by AI tools in an audit, the MOST important immediate consideration is that the data format and syntax align with the requirements of the AI tools (C).
Without correct formatting (e.g., structured fields, correct data types, consistent delimiters, proper encoding), AI tools may fail, misinterpret data, or generate unreliable results. AAIA's domain on AI in audit processes stresses data preparation, cleansing, and transformation as critical steps before applying AI analytics.

NEW QUESTION # 123
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