ISACA Advanced in AI Audit (AAIA)

The ISACA Advanced in AI Audit (AAIA) is an elite, audit-specific certification launched in May 2025. It is designed for experienced auditors to validate their ability to evaluate AI risks, navigate governance challenges, and leverage AI tools within the audit function.

---------- Question 1

An auditor is reviewing the AI solution development lifecycle. Which phase is most appropriate for conducting adversarial testing to identify security vulnerabilities like evasion attacks?

1. The initial feasibility study and business case development.
2. The data collection and labeling phase.
3. The testing and validation phase prior to deployment.
4. The decommissioning phase when the model is being retired.

---------- Question 2

An auditor is reviewing the AI Model Risk Management program. Which of the following findings would most likely indicate a failure in the model validation process?

1. The model was validated by the same team that developed the algorithm to ensure continuity
2. The validation report includes a sensitivity analysis of the model input variables
3. The model inventory is updated quarterly instead of in real-time
4. The validation process used the same dataset for both training and performance testing

---------- Question 3

An auditor is reviewing an AI-based recruitment tool. Which finding would most likely indicate a failure in the organizations AI-related awareness program?

1. The server is located in a high-security data center
2. HR managers do not understand how to interpret the AIs diversity metrics
3. The software was purchased using a credit card
4. The AI tool uses a cloud-based database

---------- Question 4

Which of the following is a specific challenge for incident response management when dealing with an AI-driven automated trading system?

1. The high speed of decision-making making manual intervention difficult.
2. The lack of electricity in the backup data center during a storm.
3. The inability to find qualified IT staff who speak multiple languages.
4. The requirement to print all trade logs on physical paper for storage.

---------- Question 5

An organization tracks the 'False Positive Rate' of its AI-based fraud detection system. How should an auditor interpret an increasing False Positive Rate?

1. It indicates the system is becoming more efficient at catching actual fraudsters.
2. It indicates an increasing burden on human investigators and potential customer dissatisfaction.
3. It is a sign that the AI has been successfully patched against all vulnerabilities.
4. It means the organization should immediately double the price of its product.

---------- Question 6

An auditor is reviewing an 'AI Audit Report'. What is the most important characteristic this report should have to be useful to stakeholders?

1. It should contain the full Python code for every algorithm tested.
2. It should translate technical AI risks into business impact and actionable recommendations.
3. It should be at least 100 pages long to demonstrate thoroughness.
4. It should avoid mentioning any negative findings to maintain team morale.

---------- Question 7

An auditor is using an AI-enabled tool to perform anomaly detection on a large dataset of financial transactions. According to Domain 3, what is the most significant advantage of this approach over traditional rule-based sampling?

1. The AI tool eliminates the need for the auditor to understand the financial business logic.
2. The AI tool can identify complex, non-linear patterns of fraud that rules might miss.
3. The AI tool is guaranteed to have a zero percent false positive rate in its findings.
4. The AI tool automatically writes the final audit report without human intervention.

---------- Question 8

An auditor is evaluating the identity and access management (IAM) for an AI training environment. Which risk is most specific to the AI development process?

1. Users might forget their passwords for the system
2. Unauthorized access could lead to poisoning of the training dataset
3. The IAM software might require a monthly update
4. Employees might use the system to check their personal email

---------- Question 9

What is the primary purpose of collecting 'Explainability' data as audit evidence for an AI system?

1. To prove that the AI was built using the most expensive software licenses.
2. To demonstrate how the model converts inputs into specific outputs for transparency.
3. To provide a list of all employees who have access to the server room.
4. To show that the AI model can run on a standard mobile phone.

---------- Question 10

While supervising an AI solution for loan approvals, an auditor notes that the model consistently rejects applicants from a specific demographic. What should be the auditors first course of action?

1. Recommend decommissioning the model immediately without further analysis.
2. Perform a disparate impact analysis to quantify potential bias.
3. Suggest increasing the interest rate for the approved applicants.
4. Delete the demographic data from the database to hide the issue.

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