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

Data drift occurs when the statistical properties of input data change over time, impacting the accuracy of an AI model. In this case, the model failed to adapt to recent demand trends, indicating that the data on which it was trained no longer reflects current behavior.

“Data drift leads to performance degradation in AI systems when real-world input data shifts away from training data patterns. Monitoring for drift is essential, particularly in dynamic environments such as retail.”

Although training diversity (A) and outlier detection (D) are relevant to accuracy, only B addresses the temporal misalignment between training data and real-time data. Overfitting (C) would more likely cause poor generalization in other contexts.

Periodic testing and monitoring for bias is a sustainable, proactive strategy aligned with best practices outlined in the AAIA™ Study Guide. This approach ensures that the AI system remains compliant over time, even as data and hiring conditions change.

“Ongoing fairness assessments help detect emerging biases and ensure that the AI model maintains equitable decision-making standards. Periodic testing also allows organizations to take corrective action before regulatory or reputational damage occurs.”

Suspending the system (B) or relying solely on external datasets (C) are temporary or limited in scope. Manual reviews (D) are effective but do not solve the root issue. Therefore, A provides a comprehensive, audit-aligned solution.

Problem framing (option D) is the process of clearly defining the purpose, scope, and desired outcomes of an AI system before it is built or deployed. According to the ISACA AAIA™ Study Guide, “problem framing is the critical first step that aligns model development and subsequent behaviors with the strategic and operational objectives of the organization.” If the AI problem is not framed in accordance with organizational goals, even a technically successful AI model could generate outputs that do not support the organization’s mission or priorities.

Algorithm debugging, data transformation, and model training are all important phases but rely on the initial problem framing to ensure their efforts are correctly directed.

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 AAIA™ 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.

Ensuring that AI tools are trained on properly licensed and documented data sets is critical to avoiding copyright infringement and legal exposure. The AAIA™ Study Guide emphasizes using platforms with certified and traceable training data to meet ethical and legal standards.

“Organizations must verify the provenance and licensing of data used to train AI systems. Platforms that certify data sources reduce the risk of using protected intellectual property without consent.”

Manual review (A) is resource-intensive and may not detect embedded copyright violations. Labeling (C) is not sufficient for legal protection. Suspension (D) may be excessive without first attempting remediation. Thus, B is the most strategic and effective recommendation.

The integrity of data fed into AI systems is a critical concern. The AAIA™ Study Guide emphasizes that validation and filtration processes are essential to mitigate the risk of data poisoning—an attack that can manipulate model behavior by injecting malicious inputs.

“Data poisoning represents a major vulnerability in AI pipelines. Effective controls include robust validation, filtration, and monitoring of training data sources. These preventive practices are essential to ensure model reliability and security.”

While options A, B, and C are important operational and training measures, only D addresses a technical risk that can directly compromise model outputs and trustworthiness.

Assessing data lineage provides insight into the origin, flow, and transformation of data across its lifecycle, which is crucial for validating data governance. The AAIA™ Study Guide states that data lineage is essential to ensure the accuracy, consistency, and trustworthiness of data used in training AI models.

“Traceability of data sources is a core tenet of effective data governance. Data lineage validation ensures data quality, prevents unauthorized modifications, and maintains auditability.”

BIA (A) focuses on impact, not data quality. Reviewing SDLC (B) is broad and may not highlight data-specific risks. Penetration testing (C) addresses security, not governance. Therefore, D is the best method.