AAIA Exam Dumps - ISACA Advanced in AI Audit (AAIA)

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.

Graph analytics is specifically designed to analyze complex relationships among people, entities, transactions, and systems. According to AAIA audit methodologies, graph analytics helps identify hidden or non-obvious relationships indicative of:

Collusion

Fraud rings

Undisclosed conflicts of interest

Influence networks

Hidden ownership structures

Correlation matrices (A) only measure linear relationships. Time series (B) identifies patterns over time, not relationships. Monte Carlo simulation (D) models uncertainty but does not uncover relational structures.

Graph analytics is the strongest AI-enabled method for mapping and auditing relational risks.

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.

AI-based coding assistants can inadvertently generate insecure code patterns, reuse vulnerable libraries, or bypass secure coding practices.

AAIA highlights security vulnerabilities as the primary risk because insecure code can lead to:

Data breaches

Injection attacks

Authentication bypasses

Supply chain compromise

Privilege escalation

Excessive reliance (A) affects productivity but not security.

Human bias (B) is possible but not as severe as security flaws.

Training complexity (D) is not a risk to AI system integrity.

Therefore, the introduction of security vulnerabilities is the greatest risk.

A steering committee is responsible for enterprise-wide strategic decisions, technology alignment, investment prioritization, and governance oversight.

AAIA states that AI implementation roadmaps must be driven by a group that can evaluate:

Organizational strategy

Technology readiness

Budget allocations

Risk appetite

Regulatory obligations

Cross-functional impacts

The risk committee (A) focuses only on risk, not implementation. Product management (C) has a narrow scope. Internal audit (D) evaluates controls, not roadmaps.

Thus, the steering committee is the appropriate authority.

Alow number of test scenarios(C) indicates insufficient testing coverage, creating a high likelihood that errors, biases, or edge-case failures remain undetected. AI systems requirebroad, diverse scenario testing, including fairness, robustness, and stress testing. AAIA highlights comprehensive testing as essential to AI reliability and risk mitigation.

Lack of standardized test formats (B) is inefficient but less severe. Missing seed documentation (D) affects reproducibility but not completeness. Lack of management testing (A) matters, but insufficient test scenarios pose thelargest direct riskto model safety and performance.

Audit trails in AI systems serve to document the inputs, processes, and outputs of AI decisions, allowing stakeholders and auditors to trace how decisions were made. The AAIAâ„¢ Study Guide identifies transparency and traceability as the core functions of an AI audit trail.

“Maintaining audit logs is critical to explainability and accountability. It ensures that decisions made by AI systems can be reconstructed and assessed for accuracy, ethics, and legality.”

Although compliance (D) is an outcome and fairness (B) a goal, only A captures the foundational purpose of audit trails in AI systems.

Ordinal encoding assigns numeric values in an order (e.g., Poodle = 1, Labrador = 2, Husky = 3). This falsely implies hierarchy or ranking among breeds, introducing bias into the model.

AAIA emphasizes that incorrect encoding of categorical variables can cause:

Artificially weighted importance

Misleading correlations

Incorrect predictions based on false orderFor non-ordered categories,one-hot encoding(D) is the correct, low-risk approach.Lack of scaling (A) is not directly relevant for categorical fields. Clustering (B) is unrelated.Thus, ordinal encoding introduces the highest risk.