Get Isaca AAIA Exam Practice Questions - Real and Updated

Answer : B

Reinforcement Learning (RL) is characterized by an 'agent' (the robot) that learns to achieve a goal in a dynamic environment through trial and error. It receives 'rewards' for correct movements and 'penalties' for errors, allowing it to optimize its 'Policy Function' over time. Unlike supervised learning (Option A), which requires a pre-labeled dataset, RL allows for continuous improvement based on real-time feedback. The ISACA AAIA Study Guide identifies RL as the standard for robotics and autonomous systems where the 'correct' action depends on the state of the physical environment.

Answer : A

This is known as 'Data Contamination' or 'Model Autophagy'. When an AI model is trained on data it (or another AI) previously generated, it creates a feedback loop. Errors, hallucinations, and biases in the generated content are re-ingested as 'ground truth,' which causes the model to 'Collapse' and lose its ability to accurately reflect the real world. The ISACA AAIA manual emphasizes that training data must be 'Primary' and 'Authentic' to ensure model integrity. Failing to distinguish between human and AI content prevents the organization from detecting and correcting systemic errors in the model's logic.

Answer : D

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.

Answer : A

'Adaptive sampling' in an AI context refers to a strategy where the sampling process evolves based on the results of previous samples. AI algorithms can analyze initial findings to identify high-risk clusters or patterns, then automatically shift the sampling focus to those areas. This is significantly more efficient than traditional systematic or statistical sampling for finding 'needles in haystacks,' such as fraud. The ISACA AAIA manual highlights that AI-enabled audit tools use adaptive logic to prioritize testing where anomalies are most likely to exist, thereby increasing the effectiveness of the audit.

Answer : B

AI-driven evidence collection should enhance efficiency and accuracy. The BEST indicator of improvement is reduced time spent gathering data with fewer errors (B), showing that AI has streamlined data extraction, consolidation, and initial validation without compromising quality. AAIA's content on AI in audit processes highlights benefits such as automation of repetitive tasks, improved coverage, and higher-quality evidence.

Extended timelines for retraining (A) suggest inefficiency rather than improvement. Eliminating human judgment (C) is neither realistic nor recommended; professional skepticism and auditor judgment remain essential. Relying on unstructured data with minimal cleansing (D) can increase risk of misinterpretation or noise. Therefore, more efficient and accurate evidence compilation is the clearest positive outcome.

ISACA, AAIA Exam Content Outline -- Domain 3: AI Auditing Tools and Techniques (efficiency and quality improvements in audit).

ISACA internal audit and data analytics guidance on AI's value in evidence collection.