AAIA Exam Dumps - ISACA Advanced in AI Audit (AAIA)

Unstructured data without standardized preprocessing (option A) creates the highest data quality risk because AI models depend heavily on the cleanliness, consistency, and structure of input data.

AAIA warns that improperly processed unstructured data leads to:

Incorrect text extraction

Lost contextual meaning

Feature extraction errors

Misclassification

Inaccurate audit evidence

Option B is a bias or relevance risk, not data quality.

Option C is a governance/training issue.

Option D is an oversight risk, not a data quality issue.

Therefore, using unstructured data without preprocessing is the most direct threat to data quality.

Ineffective anomaly detection means the system fails to recognize abnormal patterns in data or model behavior. The GREATEST risk isundetected data poisoning(B), which jeopardizesdata integrityand leads to corrupted, biased, or unsafe AI decisions. AAIA emphasizes that anomaly detection is critical for identifying tampering, data drift, or malicious manipulation.

Configuration inconsistencies (A) are operational issues but far less damaging. Slower drift response (C) can cause performance degradation but is not as severe as undetected poisoning. Reporting standard failures (D) are compliance risks—not as critical as compromised decision integrity.

Thus, undetected poisoning poses thehighest riskdue to its direct impact on model trustworthiness and safety.

Ethical risk begins with the nature of the data being used. The sensitivity and origin of training data (option B) determine whether the model risks violating privacy, perpetuating historical bias, or using data that was collected without proper consent.

AAIA identifies data provenance, data sensitivity, and lawfulness of processing as central to ethical AI. Training data that contains sensitive attributes (e.g., health, ethnicity, gender, financial status) must be reviewed carefully for compliance and ethical impacts.

Option A (diverse outputs) relates to performance, not ethics. Option C (update frequency) is part of lifecycle management, not ethical sourcing. Option D (cleaning methods) ensures quality but does not address ethical risk if the underlying data is inappropriate or unlawfully sourced.

Thus, sensitivity and origin of training data are the primary ethical factors.

Liveness detection is the most effective countermeasure against deepfakes in video-based verification. AI-based liveness detection analyzes facial movement, micro-expressions, and other biometric cues to differentiate real humans from manipulated video content.

“To protect against identity spoofing and deepfake exploitation, biometric systems must incorporate liveness detection protocols capable of detecting synthetic imagery or falsified video data.”

Encryption (C) protects data at rest and in transit but does not prevent impersonation. Discontinuation (D) may not be necessary if effective countermeasures like B are in place.

Only reviewing an ML model’s performance one time prior to migrating to production (option C) is of greatest concern. The AAIA™ Study Guide emphasizes that “AI and ML models require continuous monitoring and periodic performance reviews in production to detect issues such as data drift, model degradation, or evolving risk factors.” A single pre-production review fails to capture these changes and risks, potentially resulting in undetected failures or compliance issues.

Periodic (including annual) and event-driven reviews are necessary to ensure ongoing model reliability.

AAIA guidance states thatfairness evaluations begin with the training data, because bias is most commonly introduced through data selection, sampling imbalances, labeling inconsistencies, or historical discrimination embedded in source data.

Thus, thefirstcourse of action is to review training data (option B) to identify:

Skewed demographic distributions

Missing or underrepresented populations

Inappropriate use of sensitive attributes

Incorrect labels or improperly encoded variables

Historical decisions that may propagate discrimination

Only after identifying the existence and nature of bias can an organization move on to remediation steps such as retraining (C) or comparing alternate model behavior (A).

Allowing customers to contest premiums (D) is a post-decision remedy, not a fairness evaluation step.

AAIA emphasizes addressing bias at theroot—the training data—before adjusting the model or outputs.

Precision measures the proportion of true positives among all positive predictions. A low precision rate indicates a high rate of false positives. The AAIAâ„¢ 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.

AI auditing techniques excel at identifying complex data patterns (option C), which is their primary advantage over manual or traditional audit approaches. The AAIA™ Study Guide states, “AI-based audit tools can process massive volumes of data at speed and depth, detecting anomalies, trends, or relationships that might be invisible to human auditors or unfeasible to uncover manually.”

AI does not fully eliminate the need for human involvement, nor does it guarantee compliance or the elimination of bias, but it can analyze intricate patterns in large, multidimensional data sets.