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ISACA AAIA Exam Syllabus Topics:

Section	Objectives
Topic 1: AI Auditing Tools and Techniques
Topic 2: AI Operations
Topic 3: AI Governance and Risk

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ISACA Advanced in AI Audit Sample Questions (Q183-Q188):

NEW QUESTION # 183
A car manufacturer uses an AI model to predict maintenance needs for its vehicles. Which of the following techniques can an IS auditor apply to MOST effectively verify the AI model's decisions to stakeholders?

A. Using neural network visualization to show how the AI model processes data through its layers
B. Utilizing support vector machines (SVM) to classify vehicles based on maintenance urgency
C. Using K-means algorithms to group vehicles based on mileage or engine temperature for maintenance patterns
D. Using local interpretable model-agnostic explanation (LIME) to analyze how specific features contribute to predictions

Answer: D

Explanation:
LIME (Local Interpretable Model-Agnostic Explanations) is a leading tool for explaining individual AI predictions by approximating the behavior of complex models with simple, interpretable ones in localized regions. The AAIATM Study Guide highlights LIME as highly effective for providing transparency and interpretability to non-technical stakeholders.
"LIME enables auditors to demonstrate how specific input features influenced an AI decision, facilitating trust and stakeholder understanding--especially in regulated or high-impact contexts."

NEW QUESTION # 184
An IS auditor is evaluating a cybersecurity system that uses "agentic AI" for autonomous response. Which of the following is MOST important to consider?

A. The agent requires network expansion to operate.
B. The agent's audit logs are lengthier and increase storage costs.
C. The agent can take automated actions that may disrupt business operations.
D. The agent reduces the need for analyst intervention.

Answer: C

Explanation:
"Agentic AI" possesses the autonomy to perform actions within a system. In a cybersecurity context, an agent might autonomously block an IP or shut down a database if it detects a threat.
The most significant risk is that a "False Positive" could trigger an automated response that causes a massive service disruption. Auditors must validate the existence of "Guardrails,"
"Human-in-the-loop" approval for high-impact actions, and "Kill Switches" to halt the agent if it behaves erratically.

NEW QUESTION # 185
When converting data categories before training an AI model, which of the following scenarios represents the GREATEST risk?

A. Creating dummy variables for the data attribute dog breed for the options labrador, terrier, beagle
B. One-hot encoding the data attribute customer rewards category for the options economy, business, first class
C. Creating dummy variables for the data attribute product flavor for the options vanilla, chocolate, strawberry, banana
D. One-hot encoding the data attribute car colors for the options red, blue, green, black, white

Answer: B

NEW QUESTION # 186
An IS auditor is testing an AI-based fraud detection system that flags suspicious transactions and finds that the system has a high false positive rate. Which of the following testing methods should be prioritized to BEST optimize the detection rate?

A. Benford's Law analysis
B. Substantive testing
C. Cross-validation testing
D. Regression testing

Answer: C

NEW QUESTION # 187
Which of the following is the MOST effective control to prevent data poisoning attacks during model training?

A. Increasing model complexity
B. Validating and sanitizing training data sources with provenance checks
C. Applying rate limiting to the inference API
D. Encrypting model weights

Answer: B

Explanation:
Data poisoning involves injecting malicious or mislabeled data into the training set. Provenance verification and data validation pipelines reduce the likelihood of compromised data reaching the model.

NEW QUESTION # 188
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