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| Topic | Details |
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| 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.
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| 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.
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| 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 (Q168-Q173):
NEW QUESTION # 168
Which of the following techniques BEST assesses an AI model's ability to generalize to new data?
- A. Failure mode effects analysis
- B. Metamorphic testing
- C. Model component unit testing
- D. K-fold cross validation
Answer: D
Explanation:
K-fold cross validationpartitions the dataset intokequally sized folds, iteratively using one fold for testing and the remaining folds for training. By rotating through all folds, the model is evaluated on multiple, non- overlapping subsets of data, providing a robust estimate of generalization performance. This process reduces the risk of overfitting to a single train-test split and is explicitly recognized in AI testing techniques and data analytics practices relevant to AAIA.
Option A (failure mode effects analysis) is useful for understanding failure impacts but not primarily for generalization. Option B (unit testing) focuses on code components, not model generalization. Option D (metamorphic testing) is valuable for validating behavior under transformations, but the standard, widely accepted approach to assessing generalization remainscross validation, making option C the best answer.
References:
ISACA,AAIA Exam Content Outline- Domain 2: Testing Techniques for AI Solutions (conventional and AI- specific testing).
ISACA AI and data analytics training content that discusses cross validation as a generalization assessment method.
NEW QUESTION # 169
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. Assess the response time to the attacks.
- B. Review the speed of learning adaptation.
- C. Evaluate the number of system logs maintained.
- D. Validate the process for handling manipulated inputs.
Answer: D
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.
NEW QUESTION # 170
During audit planning, an IS auditor reviews the correlation matrix. Which variable pair from an electrical generation facility has the MOST significant correlation?
- A. Daily precipitation and generator efficiency is 0.09
- B. Temperature and generator effectiveness is -0.85
- C. Rotational speed and tool wear is 0.56
- D. Electricity demand and machine torque is 0.0
Answer: B
Explanation:
A correlation of -0.85 (option C) represents a strong negative correlation, indicating a meaningful inverse relationship. In AI model development, high-magnitude correlations (positive or negative) strongly influence feature selection and model behavior.
AAIA emphasizes that auditors must understand correlation strength to evaluate:
* Feature relevance
* Model weight justification
* Risk of multicollinearity
* Data quality and representativeness
The other correlations are weak or negligible.
Thus, the pair with -0.85 is the most significant.
References:
AAIA Domain 1: AI Data Relationships
AAIA Domain 2: Data Understanding and Statistical Foundations
NEW QUESTION # 171
Which of the following is the PRIMARY objective of AI governance?
- A. Ensuring controls over AI are designed well and operate effectively
- B. Defining clear roles and responsibilities for AI development, use, and oversight
- C. Implementing compliance and ethics controls for AI initiatives
- D. Promoting a positive return on investment (ROI) from AI projects
Answer: B
Explanation:
The AAIA™ Study Guide defines the primary objective of AI governance as establishing structure and accountability for AI initiatives. This includes clearly assigning responsibilities across development, deployment, risk management, and auditing roles to ensure that AI is used responsibly and transparently.
"AI governance establishes the policies, roles, and oversight structures that guide the ethical and secure deployment of AI. Clear accountability helps prevent unauthorized use and ensures strategic alignment." Options A and C are essential components of governance but are not its core definition. Option D is a business outcome, not a governance goal. Thus, B is the most comprehensive and accurate objective.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Governance and Risk Management," Subsection: "Governance Objectives and Structures"
NEW QUESTION # 172
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 local interpretable model-agnostic explanation (LIME) to analyze how specific featurescontribute to predictions
- B. Using K-means algorithms to group vehicles based on mileage or engine temperature for maintenance patterns
- C. Utilizing support vector machines (SVM) to classify vehicles based on maintenance urgency
- D. Using neural network visualization to show how the AI model processes data through its layers
Answer: A
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 AAIA™ 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." Options A, B, and C are technical modeling techniques but do not prioritize stakeholder-friendly explanation.
Therefore, D is best for transparency.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI in Audit Processes," Subsection: "Explainability Tools and Stakeholder Communication"
NEW QUESTION # 173
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