Pass the Isaca Advanced in AI Audit AAIA Questions and answers with CertsForce

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.

The strongest demonstration of compliance with privacy principles—especially those emphasized in AAIA, such aslawfulness, transparency, and consent—is obtainingexpressed customer consent(B) before sharing data with third parties. Consent ensures that data processing aligns with legal and ethical requirements, reducing risks related to regulatory violations, unauthorized processing, and loss of customer trust.

Option A and C relate only to disclosure and transparency, which are necessary but insufficient. Option D concerns vendor security controls, which are important but do not address the legal basis for sharing data.Explicit, informed consentis the highest standard of compliance for third-party data sharing.

AAIA guidance states thatfairness evaluations begin with the training data, because bias is most commonly introduced through data selection, sampling imbalances, labeling inconsistencies, or historical discrimination embedded in source data.

Thus, thefirstcourse of action is to review training data (option B) to identify:

Skewed demographic distributions

Missing or underrepresented populations

Inappropriate use of sensitive attributes

Incorrect labels or improperly encoded variables

Historical decisions that may propagate discrimination

Only after identifying the existence and nature of bias can an organization move on to remediation steps such as retraining (C) or comparing alternate model behavior (A).

Allowing customers to contest premiums (D) is a post-decision remedy, not a fairness evaluation step.

AAIA emphasizes addressing bias at theroot—the training data—before adjusting the model or outputs.

Transparency is a foundational principle for AI governance and effective risk management. When evaluating AI systems, IS auditors must assess not just how an algorithm functions, but also whether its processes and decisions are understandable and explainable. According to the ISACA Advanced in AI Audit™ (AAIA™) Study Guide, "algorithmic transparency—enabling auditors and stakeholders to see and understand the rationale behind automated decisions—is essential for ensuring accountability, fairness, and compliance with both organizational policies and regulatory requirements."

Transparent justification of decisions (option C) directly supports an auditor’s ability to evaluate the appropriateness, logic, and fairness of the AI’s outputs. This is critical in identifying bias, ensuring ethical considerations are met, and verifying that outcomes are consistent with intended objectives. Without transparent justification, it is difficult for an IS auditor to independently assess whether decisions are being made based on valid, legal, and ethical grounds.

By contrast, options A, B, and D are either standard operational aspects or supportive features, but they do not directly empower auditors to evaluate or scrutinize the algorithm’s decision-making process. The Study Guide explicitly highlights, “Effective AI governance frameworks require that systems provide clear, interpretable explanations for their decisions, supporting auditability and accountability across the AI lifecycle.”

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.

Bias in AI decision-making is one of the most critical risks, particularly when AI influences areas like hiring, lending, or healthcare. The AAIA™ Study Guide highlights the ethical and operational consequences of biased models, which may lead to discrimination, legal liability, and reputational damage.

“Bias in AI outputs can originate from skewed training data or flawed algorithms. Auditors must evaluate whether mitigation techniques, such as bias detection and fairness testing, are in place.”

While costs (A) and industry maturity (B) are considerations, they do not pose the same systemic ethical risk. Resistance (D) is a change management issue. C represents the most impactful and widespread risk.

Precision measures the proportion of true positives among all positive predictions. A low precision rate indicates a high rate of false positives. The AAIA™ Study Guide recommends using precision when the goal is to minimize incorrect positive alerts, which is especially relevant in fraud detection, cybersecurity, and classification models.

“Precision is the key metric when false positives have a significant operational cost. It provides insight into the model’s ability to avoid incorrect positive classifications.”

Accuracy and recall give broader insights, but only precision directly measures false positive risk. Completeness is not a standard ML metric.

When an AI system begins givingincorrect financial advice, the FIRST step is tosuspend the chatbot(D) to prevent ongoing harm. AAIA emphasizes protecting users from unsafe decisions as the top priority in AI operations.

Suspension allows the organization to:

Stop distribution of harmful advice

Assess impact and identify faulty API dependencies

Prevent regulatory or customer harm

Conduct root-cause analysis safely

Retraining (C) is corrective but must occurafterassessment. Adding rules (B) risks masking deeper issues. Speed patches (A) are irrelevant to correctness.

False positives in fraud detection AI systems often stem from poorly optimized hyperparameters. Hyperparameters control aspects of the model’s learning process such as the learning rate, decision thresholds, and complexity penalties. When these parameters are not tuned correctly, the model can become overly sensitive and flag normal behavior as suspicious, leading to customer complaints.

“Hyperparameter tuning is essential to balance sensitivity and specificity in AI models. Improper tuning can result in a high rate of false positives or negatives, particularly in systems like fraud detection that require nuanced pattern recognition.”

Options A and B relate to data governance but do not directly cause false positives in predictions. Option C (compute scale training) may affect model efficiency, not accuracy. Thus, D is the most appropriate answer.

Using AI to make employment decisions such as driver termination or retention introduces significant ethical risks. If based solely on performance metrics without context or human review, such systems can lead to unfair treatment or discrimination—violating principles of transparency and due process.

“Automating workforce decisions must be approached cautiously to prevent discriminatory outcomes. Ethical AI governance requires oversight when AI is used for employment-impacting decisions.”

A, C, and D involve business optimization without directly affecting individual employment rights. Therefore, B poses the greatest ethical risk.