Free Practice Questions for Isaca AAIA Exam

The F1 score is the harmonic mean of precision and recall, offering a balanced measure of model accuracy, especially when there is an imbalance in the classes (e.g., more “no-maintenance” than “needs-maintenance” outcomes). According to the AAIA™ Study Guide, the F1 score is used to evaluate how well the model identifies true positives while balancing the risk of false positives and false negatives.

“An F1 score summarizes the model's ability to correctly identify relevant events—in this case, true maintenance needs. A 76% F1 score means the model is relatively balanced and effective at catching maintenance requirements without generating too many false alerts.”

Thus, D is correct. Option A misrepresents recall as predictive accuracy. Option B misinterprets accuracy. Option C has no direct basis in the excerpt.

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.

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.

In the context of AI-driven audit planning, the AAIA™ Study Guide identifies incomplete or inaccurate data as the most significant risk. If the data input into AI tools is missing, outdated, or inconsistent, the model's suggestions for risk prioritization or control testing will be flawed.

“Audit planning supported by AI tools is only as strong as the underlying data. Incomplete data may cause incorrect risk assessments or misallocate audit resources, undermining audit effectiveness.”

While scope creep and cost are common concerns in project management, and limited AI knowledge can be mitigated through training, only incomplete data directly impacts the validity of audit planning outputs.

The AAIA™ Study Guide emphasizes that regular algorithmic audits are critical for identifying unintended consequences such as the promotion of harmful or biased content. This proactive approach helps maintain trust and ensure that algorithmic decisions align with organizational values and ethical standards.

“Auditing and monitoring AI models regularly helps detect and correct bias, drift, or other unintended behavior. It is essential for high-impact AI systems like content recommendation engines.”

While content customization (A) and user consent (C) are helpful, they don’t prevent bias propagation. Suspension (B) may halt engagement and isn’t sustainable. Therefore, D is the most balanced and strategic solution.

Data cleaning is a foundational task in the AI development lifecycle. The AAIA™ Study Guide identifies data quality—ensuring completeness, accuracy, consistency, and correctness—as critical to building effective and unbiased AI systems. Cleaning the data involves removing duplicates, correcting errors, addressing missing values, and standardizing formats.

“Data cleaning is a prerequisite for effective training and evaluation. Poor-quality data leads to inaccurate or misleading model outputs, increasing operational and ethical risks.”

While training (D) is essential, it must occur only after the data has been adequately prepared. Stratification (A) supports certain modeling approaches but is secondary to data integrity. Therefore, C is the most important task at the data-gathering stage.

The AAIA™ Study Guide recommends using validation tools to fine-tune and evaluate ML models, particularly when high false positives undermine operational efficiency. ML validation can identify threshold adjustments, retraining needs, or feature misweighting contributing to excessive alerting.

“Model validation enables organizations to quantify performance, reduce false alarms, and recalibrate AI behavior to align with operational needs and threat landscapes.”

While logs (A) and benchmarks (B) help with diagnosis, they don’t improve the model. Penetration testing (C) evaluates detection, not alert noise. D is the most effective solution.