Isaca Updated AAIA Exam Questions and Answers by yazan

The primary goal of any AI system is to provide predictions or classifications that support business decisions. The AAIA™ Study Guide highlights that model accuracy—especially when validated against actual outcomes—is the most reliable indicator of whether the AI supports organizational goals effectively.

“Accuracy, precision, and recall are foundational metrics that indicate whether a model is performing in line with its intended objectives. High user engagement or retraining frequency does not confirm effective decision support unless the outputs are correct.”

Cost and user numbers offer useful operational insights but do not reflect the alignment of AI performance with strategic goals. Thus, D is the most meaningful KPI in this context.

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.

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.

The percentage of missing values (option B) directly reflects data collection issues. Missing or incomplete data can degrade model performance, distort feature distributions, and create biased or inaccurate predictions.

AAIA stresses that auditors must evaluate:

Completeness

Validity

Accuracy

Consistency

Missing values signal failures in upstream processes, including sensors, user inputs, integrations, or data pipelines.

The other metrics are unrelated to raw data integrity:

Epochs (A) refer to training cycles.

Percentage of training data (C) concerns dataset partitioning, not quality.

True positives (D) relate to model performance, not data collection quality.