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

Testing the AI model with a curated and representative sample data set allows auditors to directly evaluate the fairness and bias of model decisions. This approach is aligned with best practices outlined in the AAIA™ Study Guide, as it enables quantifiable analysis of model behavior across different demographics or input scenarios.

“To assess fairness, auditors should use controlled data sets to evaluate whether model outputs disproportionately impact specific groups. This empirical testing provides stronger evidence than qualitative methods.”

While metadata (A) and developer interviews (C) can supplement findings, only B provides objective, reproducible evidence. Option D may reflect real-world interactions but lacks the control and consistency required in an audit.

Risk assessments identify potential threats and vulnerabilities in AI systems and support the development of mitigation strategies. According to the AAIA™ Study Guide, the main objective is not just identification of risks but to proactively design controls that minimize impact and ensure ethical and regulatory compliance.

“The output of an AI risk assessment should lead to actionable mitigation strategies, supporting the secure and responsible deployment of AI solutions across business processes.”

Options A, C, and D are components of governance and monitoring, but B addresses the core intent of risk assessments.

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.

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.

In healthcare AI applications, protecting patient data is critical. The AAIA™ Study Guide identifies anonymization as one of the most effective strategies to preserve privacy and maintain data integrity. When combined with quality checks, it ensures data accuracy and compliance with health data protection regulations (e.g., HIPAA, GDPR).

“Anonymizing sensitive data removes identifying attributes, significantly reducing risk if data is accessed or leaked. Ongoing data quality checks ensure the integrity and utility of the anonymized dataset.”

While encryption (A) and access controls (C) are necessary technical safeguards, D provides the strongest dual assurance of privacy and accuracy. Option B focuses on model management rather than data security.

The AAIA™ Study Guide reinforces that regular ethical reviews are essential to uphold human rights, prevent discriminatory outcomes, and ensure systems function within the boundaries of fairness and legality. While aligning with values (B) and preventing drift (D) are secondary benefits, the primary ethical imperative is the protection of individuals' rights and freedoms.

“Ethical reviews ensure AI systems do not violate rights related to privacy, fairness, access, and due process. This is foundational in building public trust and avoiding legal liabilities.”

Option C is the clearest expression of this responsibility. Performance and alignment with values are important but secondary to ensuring human-centric safeguards.

Using postal codes as a primary factor in credit scoring raises concerns of geographic and socioeconomic bias. Postal codes can serve as proxies for race, ethnicity, or income level—potentially violating fair lending laws and ethical guidelines.

“Auditors should flag models that rely on proxies for protected attributes. Postal codes are high-risk features that may inadvertently lead to redlining or other discriminatory practices.”

A, B, and C are more justifiable under fair lending guidelines. Thus, D presents the highest concern.

The AAIA™ Study Guide emphasizes that the foundation of any high-performing and ethical AI system lies in the quality and integrity of its data. For high-risk AI systems, such as those used in healthcare, finance, or criminal justice, it is essential to base models on trustworthy data. This ensures reliable predictions, reduces bias, and mitigates risk.

“Trustworthy datasets are characterized by accuracy, completeness, consistency, and ethical sourcing. In high-risk AI applications, ensuring data quality at every stage is crucial to system reliability and compliance.”

While backups, user training, and MFA are important for security and operational resilience, they do not address the core challenge of ensuring model accuracy and fairness at the development and design phase. Therefore, option C is the most effective practice.

Supervised learning is a foundational type of machine learning in which the model is trained on a labeled dataset. According to the AAIA™ Study Guide, labeled data includes input features along with the correct output, enabling the model to learn the mapping function accurately.

“In supervised learning, models learn from input-output pairs provided in the training data. This method enables predictive modeling tasks such as classification and regression.”

Unlabeled data (A) is used in unsupervised learning; clustered data (B) is a technique rather than a data type; and randomized data (D) refers to distribution strategy, not labeling. Hence, labeled data is the correct answer.

Anomaly monitoring detects irregularities or deviations in input data or model outputs, which are key indicators of model drift. According to the AAIA™ Study Guide, continuous anomaly detection is one of the most effective methods for identifying when a model is no longer functioning as expected due to changes in the data environment.

“Monitoring for output anomalies enables early identification of model drift. This proactive approach allows organizations to retrain or adjust models before significant performance degradation occurs.”

Configuration standardization (A) and documentation reviews (C) support governance but don’t detect changes. Retraining (D) is a remediation step, not a detection mechanism. Therefore, B is correct.