AAIA Exam Dumps - Isaca Advanced in AI Audit Questions and Answers

Transparency in AI, especially in regulated sectors like finance, is best achieved by using explainability tools that interpret the internal workings and outputs of complex ML models. The AAIA™ Study Guide highlights model explainability as essential for both auditors and regulators to understand why certain decisions are made.

“Explainability tools allow auditors to trace predictions back to input features, helping verify fairness, logic, and compliance. These tools support transparency and trustworthiness in black-box models.”

While documentation (A) and reporting (C) are part of governance, D directly supports decision-level clarity. Dashboards (B) show results, not rationale.

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.

K-means clustering is anunsupervised learningtechnique that groups data based on similarity. Discovering a cluster withhigh spending but low product diversityis a plausible and meaningful business insight: it may representloyal customers who repeatedly purchase a narrow range of products. The auditor should therefore recommend treating this cluster as apotentially valid segment(B), subject to further business analysis and controls where appropriate.

Option A is incorrect because this pattern does not imply algorithm failure. Option C (adding more clusters) might overcomplicate the segmentation without evidence that the current clustering is deficient. Option D misunderstands the purpose of clustering; a supervised model would require labeled outcomes and is not necessarily “more accurate” for exploratory segmentation. AAIA’s content on AI in audit processes stresses that auditors must interpret AI-driven insights critically, not assume anomalies equal errors.

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.

AAIA emphasizes thattransparency and interpretabilityrequire clear explanations of how the model functions, what features drive predictions, and how decisions are derived.

Creating documentation and visual interpretability tools (option C) provides:

Feature importance breakdowns

Decision pathway visualizations

Examples of prediction reasoning

Plain-language explanations for nontechnical stakeholders

Evidence that can be validated during audits

While training stakeholders (A) is helpful, it does not make the model itself clearer.

A rule-based system (B) supports validation, not interpretability.

Simplifying the model (D) may affect accuracy and is not necessary if documentation and interpretability tooling solve the issue.

Therefore, the best approach is to improve interpretability through clear documentation and visualization.

An AI model inventory documents the models in use, their purposes, and how they support specific business functions. According to the AAIA™ Study Guide, maintaining a comprehensive AI model inventory allows auditors to trace model objectives, performance metrics, and use cases back to business goals.

“A well-maintained AI model inventory supports governance and alignment by offering a centralized view of model functions, business integration, and ownership. It ensures transparency and strategic coherence.”

While policies and assessments are important, only the inventory directly shows which AI models exist and their connection to organizational objectives.

The AAIA™ Study Guide emphasizes that AI implementations introduce dynamic and non-deterministic elements into systems, increasing the risk associated with changes. A comprehensive, AI-specific risk assessment is therefore the most critical component of a change management program to ensure that updates, retraining, or parameter adjustments do not introduce vulnerabilities or unintended consequences.

“Risk assessments tailored to AI are crucial because changes to models, training data, or infrastructure can affect performance, ethical compliance, or expose the system to new threats. A standard IT change review is often insufficient.”

While having a governance committee (A) and reviewing documentation (B) are important supporting practices, only option C directly mitigates the core risks of AI system change. Ethics training (D) supports awareness but is not directly tied to change control.

If AI-generated audit reports containfalse conclusions, the integrity of audit evidence and overall assurance is compromised. According to AAIA, when AI systems compromiseaccuracy of audit outputs, the immediate step should be tosuspend use(B) to prevent further incorrect or misleading reporting.

This allows auditors to:

Investigate the root cause of incorrect conclusions

Validate the model’s training data, rules, and prompt structures

Reassess controls, safeguards, and human review processes

Prevent reliance on unreliable AI-generated audit material

Increasing context (A) or reducing creativity (C) may help generative models but do not guarantee correction of fundamental errors. Updating SLAs (D) is administrative and does not solve the immediate integrity issue.

Including AI-specific disruption scenarios in the organization’s Disaster Recovery Plan (DRP) ensures preparedness for worst-case events affecting AI systems. The AAIA™ Study Guide recommends tailoring business continuity and DR strategies to cover model unavailability, data corruption, and AI-specific dependencies.

“AI disruptions can arise from unique causes such as model corruption, adversarial attacks, or drift. Integrating these into the DRP allows for effective, scenario-specific responses and minimizes downtime.”

Tabletop exercises (A) are valuable for preparedness but are less comprehensive than scenario planning. Kill chains (B) are security-specific. KRIs (C) aid in monitoring but don’t ensure recovery. Therefore, D offers the most robust mitigation.

In areal-time predictionenvironment (e.g., fraud detection, medical triage, automotive risk),latency and inference speeddirectly affect safety, accuracy, and business performance.

If the SLAdoes not include guarantees for latency, the model may fail to deliver predictions in time, leading to:

Incorrect or delayed decisions

Transaction failures

Safety incidents in time-sensitive use cases

Compliance violations in regulated domains

Although audit trails (B) and governance frameworks (D) are important, theoperational riskrelated to latency is the most immediate and severe.

Limited AI skills among cloud employees (A) is not directly relevant since customers maintain operational responsibility.