Free Isaca AAIA Practice Exam with Questions & Answers | Set: 2

While cost, speed, and infrastructure are practical considerations, the AAIA™ Study Guide stresses that ethical implications must be the top priority. Implementing AI without evaluating potential harm, bias, discrimination, or privacy concerns can result in legal, social, and reputational damage.

“Ethical implications should be central to AI implementation decisions. Responsible AI practices involve evaluating societal impact, transparency, accountability, and fairness before deployment.”

Therefore, D is the most essential consideration.

Natural Language Processing (NLP) enables systems to understand, summarize, and extract relevant insights from unstructured text data such as contracts, emails, or reports. The AAIA™ Study Guide recognizes NLP as the optimal tool for document review during audit planning or risk assessment phases.

“NLP techniques can efficiently process large volumes of documentation, highlight key clauses, and identify inconsistencies or risk indicators. This enhances audit productivity and thoroughness.”

While RPA (A) automates repetitive tasks, it doesn’t interpret content. Predictive analytics (C) forecasts trends, and autoregressive models (B) generate sequences, not summarize documents.

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

The AAIA™ Study Guide outlines the primary goal of auditing AI systems as ensuring that they operate ethically, transparently, and fairly. This includes identifying potential biases in decision-making and confirming that the rationale behind outcomes can be explained and understood.

“The key focus of an AI audit is to evaluate whether the system performs within the parameters of fairness, legality, and accountability. This involves testing for bias and ensuring decision-making transparency.”

Options B, C, and D refer to performance and usability concerns, which are secondary to the ethical and governance-focused purpose of AI audits.

The AAIA™ Study Guide reinforces that regular ethical reviews are essential to uphold human rights, prevent discriminatory outcomes, and ensure systems function within the boundaries of fairness and legality. While aligning with values (B) and preventing drift (D) are secondary benefits, the primary ethical imperative is the protection of individuals' rights and freedoms.

“Ethical reviews ensure AI systems do not violate rights related to privacy, fairness, access, and due process. This is foundational in building public trust and avoiding legal liabilities.”

Option C is the clearest expression of this responsibility. Performance and alignment with values are important but secondary to ensuring human-centric safeguards.

Data drift occurs when the statistical properties of input data change over time, impacting the accuracy of an AI model. In this case, the model failed to adapt to recent demand trends, indicating that the data on which it was trained no longer reflects current behavior.

“Data drift leads to performance degradation in AI systems when real-world input data shifts away from training data patterns. Monitoring for drift is essential, particularly in dynamic environments such as retail.”

Although training diversity (A) and outlier detection (D) are relevant to accuracy, only B addresses the temporal misalignment between training data and real-time data. Overfitting (C) would more likely cause poor generalization in other contexts.

Transparency in AI systems is a key requirement to ensure trust, accountability, and ethical compliance. According to the ISACA AAIA™ Study Guide under the "AI Governance and Risk Management" section, understanding the decision-making process of an AI system falls under the principle of explainability. Explainability refers to the degree to which an observer can understand the internal mechanics of an AI system and the rationale behind its outputs.

“Reviewing the explainability of AI outputs allows auditors and stakeholders to determine whether model decisions are interpretable and justifiable. High transparency means stakeholders can trace how and why a decision was made.”

While algorithm complexity and computational cost are technical considerations, they do not directly facilitate the audit of decision-making transparency. Similarly, training data diversity is essential for bias reduction but does not explain how decisions are derived. Therefore, option D is the most aligned with auditing transparency.

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.

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.

The AAIA™ Study Guide defines the core purpose of machine learning as the ability to enable systems to learn from data and make decisions or perform tasks that typically require human cognitive functions. ML allows AI systems to identify patterns, learn from historical data, and automate complex decision-making.

“Machine learning empowers systems to simulate aspects of human intelligence, including pattern recognition, language understanding, and decision-making. It forms the backbone of many AI applications designed to replace or augment human tasks.”

While visual analysis (A) and statistical inference (D) are functions of ML, they are subsets—not primary goals. Explainability (B) is important but is not a core ML function. Thus, C best represents the primary objective.