Isaca Updated AAIA Exam Questions and Answers by ariel

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.

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.

According to the ISACA AAIA™ Study Guide, a " Data Governance Framework " is the foundational control. It provides the policies, roles (stewards, owners), and standards necessary to manage the entire data lifecycle. Without a framework, activities like inventorying (Option C) or labeling (Option D) are ad-hoc and lack accountability. A formal framework ensures that data management is consistent, compliant with privacy laws, and aligned with the organization’s risk tolerance. It is the " enabling " control that makes all other data quality and security metrics meaningful and enforceable.

Images from social media platforms (C) pose the greatest risk to data integrity because:

Images may be heavily filtered or edited

Lighting, scale, and resolution are inconsistent

Metadata is unreliable

Medical diagnosis labels (if any) are unverified

Context is unknown and may include misinformation

Sources cannot be validated or authenticated

AAIA stresses that medical AI systems require high-quality, clinically validated datasets with clear provenance, accuracy, and consent.

Research facilities (A) and dermatologist cohorts (D) provide medically reliable, high-integrity data.

Open-source augmentation files (B) are synthetic and secondary, but still safer than uncontrolled, unlabeled social media images.