ISACA Advanced in AI Audit (AAIA) Version: Demo [ Total Questions: 10] Web: www.certsout.com Email: support@certsout.com Isaca AAIA IMPORTANT NOTICE Feedback We have developed quality product and state-of-art service to ensure our customers interest. If you have any suggestions, please feel free to contact us at feedback@certsout.com Support If you have any questions about our product, please provide the following items: exam code screenshot of the question login id/email please contact us at and our technical experts will provide support within 24 hours. support@certsout.com Copyright The product of each order has its own encryption code, so you should use it independently. Any unauthorized changes will inflict legal punishment. We reserve the right of final explanation for this statement. Isaca - AAIA Certs Exam 1 of 7 Pass with Valid Exam Questions Pool A. B. C. D. A. B. Category Breakdown Category Number of Questions Ethical and Legal Considerations in AI 1 AI Governance and Risk Management 4 AI in Audit Processes 3 AI Operations and Performance 1 AI Fundamentals and Technologies 1 TOTAL 10 Question #:1 - [Ethical and Legal Considerations in AI] Which of the following is MOST important for an IS auditor to review during an AI system audit in order to determine compliance with intellectual property and data rights? Data performance metrics Data usage agreements Use of open-source intellectual property Model runtime efficiency logs Answer: B Explanation To assess compliance with intellectual property (IP) and data rights, the IS auditor must review documented data usage agreements that specify ownership, licensing, consent, and limitations of use. The AAIA™ Study Guide underscores the importance of verifying that the data used to train or feed AI models is obtained and utilized within legal and contractual boundaries. “Auditors must review data usage agreements to validate whether the organization has appropriate rights to use, distribute, or transform data inputs, especially where third-party or sensitive data is involved.” While open-source usage (C) is a concern, only B provides legal clarity. Metrics (A) and logs (D) reflect performance—not legal compliance. Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: “Ethical and Legal Considerations in AI,” Subsection: “Data Rights, Licensing, and Intellectual Property” Question #:2 - [AI Governance and Risk Management] Which of the following is the PRIMARY objective of AI governance? Implementing compliance and ethics controls for AI initiatives Defining clear roles and responsibilities for AI development, use, and oversight Isaca - AAIA Certs Exam 2 of 7 Pass with Valid Exam Questions Pool C. D. A. B. C. D. Ensuring controls over AI are designed well and operate effectively Promoting a positive return on investment (ROI) from AI projects Answer: B Explanation The AAIA™ Study Guide defines the primary objective of AI governance as establishing structure and accountability for AI initiatives. This includes clearly assigning responsibilities across development, deployment, risk management, and auditing roles to ensure that AI is used responsibly and transparently. “AI governance establishes the policies, roles, and oversight structures that guide the ethical and secure deployment of AI. Clear accountability helps prevent unauthorized use and ensures strategic alignment.” Options A and C are essential components of governance but are not its core definition. Option D is a business outcome, not a governance goal. Thus, B is the most comprehensive and accurate objective. Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: “AI Governance and Risk Management,” Subsection: “Governance Objectives and Structures” Question #:3 - [AI in Audit Processes] Which of the following is the MOST effective way an IS auditor could use generative AI to plan an audit of a new database storing transactional data? Identifying separation of duties conflicts for database data changes Developing architecture diagrams Identifying technology-specific risk and considerations Summarizing meeting transcripts from interviews with database administrators (DBAs) Answer: C Explanation Generative AI excels at synthesizing large datasets and technical documentation into understandable insights. The AAIA™ Study Guide recommends leveraging generative AI to identify domain-specific risks and control considerations by analyzing complex environments and correlating them with industry risk patterns. “AI can assist auditors during planning by generating tailored risk profiles for technologies under review, helping prioritize audit focus and scoping.” While summarizing interviews (D) and creating diagrams (B) are helpful, only C directly informs audit planning with actionable intelligence. A (separation of duties) is a later-stage control assessment. Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: “AI in Audit Processes,” Subsection: “Generative AI Use in Planning and Scoping” Isaca - AAIA Certs Exam 3 of 7 Pass with Valid Exam Questions Pool A. B. C. D. A. B. C. D. Question #:4 - [AI Operations and Performance] When utilizing a machine learning (ML) model to predict whether a wind turbine electricity generator will fail, which model evaluation metric should be the PRIMARY focus? Precision Specificity Accuracy Recall Answer: D Explanation In predictive maintenance use cases—such as detecting turbine failure—the most critical concern is identifying as many actual failures as possible to prevent catastrophic events. The AAIA™ Study Guide emphasizes that in such high-risk scenarios, Recall is the most appropriate metric because it measures the proportion of true positives correctly identified. “Recall is critical in scenarios where missing a positive instance (e.g., a failure) is costly or dangerous. It ensures that most real issues are caught by the model, even at the expense of some false positives.” Precision measures correctness of positive predictions, specificity measures true negatives, and accuracy may be misleading if the data is imbalanced. Thus, D (Recall) is most appropriate. Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: “AI Operations and Performance,” Subsection: “Evaluation Metrics and Predictive Accuracy” Question #:5 - [AI in Audit Processes] Which of the following will provide the BEST evidence to support the alignment of an AI model with an organization's business objectives? AI model vulnerability assessment AI change management requests AI model inventory AI acceptable use policy Answer: C Explanation Isaca - AAIA Certs Exam 4 of 7 Pass with Valid Exam Questions Pool A. B. C. D. An AI model inventory documents the models in use, their purposes, and how they support specific business functions. According to the AAIA™ Study Guide, maintaining a comprehensive AI model inventory allows auditors to trace model objectives, performance metrics, and use cases back to business goals. “A well-maintained AI model inventory supports governance and alignment by offering a centralized view of model functions, business integration, and ownership. It ensures transparency and strategic coherence.” While policies and assessments are important, only the inventory directly shows which AI models exist and their connection to organizational objectives. Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: “AI in Audit Processes,” Subsection: “Evidence and Documentation of AI Strategy Alignment” Question #:6 - [AI in Audit Processes] A car manufacturer uses an AI model to predict maintenance needs for its vehicles. Which of the following techniques can an IS auditor apply to MOST effectively verify the AI model's decisions to stakeholders? Using neural network visualization to show how the AI model processes data through its layers Using K-means algorithms to group vehicles based on mileage or engine temperature for maintenance patterns Utilizing support vector machines (SVM) to classify vehicles based on maintenance urgency Using local interpretable model-agnostic explanation (LIME) to analyze how specific features contribute to predictions Answer: D Explanation LIME (Local Interpretable Model-Agnostic Explanations) is a leading tool for explaining individual AI predictions by approximating the behavior of complex models with simple, interpretable ones in localized regions. The AAIA™ Study Guide highlights LIME as highly effective for providing transparency and interpretability to non-technical stakeholders. “LIME enables auditors to demonstrate how specific input features influenced an AI decision, facilitating trust and stakeholder understanding—especially in regulated or high-impact contexts.” Options A, B, and C are technical modeling techniques but do not prioritize stakeholder-friendly explanation. Therefore, D is best for transparency. Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: “AI in Audit Processes,” Subsection: “Explainability Tools and Stakeholder Communication” Question #:7 - [AI Fundamentals and Technologies] Isaca - AAIA Certs Exam 5 of 7 Pass with Valid Exam Questions Pool A. B. C. D. A. B. C. D. Which of the following is MOST important to have in place when initially populating data into a data frame for an AI model? The box charts, histograms, scatterplots, and Venn diagrams that identify correlations and outliers The code for separating data into training and testing data sets An analysis of exploratory data that checks for incorrect data types, null values, and duplicate entries An approved risk assessment for including, excluding, or subsequently dropping data attributes from the model Answer: C Explanation Exploratory Data Analysis (EDA) is critical during the initial stages of AI model development. According to the AAIA™ Study Guide, performing EDA—including identifying null values, incorrect data types, or duplicates—ensures that the data fed into the model is clean and reliable. “Initial data frames should be subject to thorough EDA to uncover data quality issues. These issues, if not addressed early, can severely affect model training and predictive accuracy.” While separating data sets (B) and visualizations (A) are important steps in later phases, C is foundational to ensure readiness for model training. Risk assessments (D) are necessary but not the first operational step. Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: “AI Fundamentals and Technologies,” Subsection: “Exploratory Data Analysis and Preprocessing” Question #:8 - [AI Governance and Risk Management] Which of the following testing techniques would BEST validate whether an organization's data governance program effectively ensures data quality and integrity for AI model training and deployment? Performing a business impact analysis (BIA) to assess the consequences of AI model failure Reviewing the organization’s AI software development life cycle documentation Conducting a penetration test to identify vulnerabilities in the model Assessing data lineage to verify the traceability of data sources Answer: D Explanation 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. Isaca - AAIA Certs Exam 6 of 7 Pass with Valid Exam Questions Pool A. B. C. D. A. B. “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. Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: “AI Governance and Risk Management,” Subsection: “Data Quality, Integrity, and Governance Practices” Question #:9 - [AI Governance and Risk Management] Which of the following is the PRIMARY benefit of implementing a robust data governance framework specific to AI solutions in an organization? It focuses on enhancing the accuracy and reliability of AI model predictions. It accelerates AI implementation timelines by fully automating data preparation processes. It fosters adherence to industry regulations while minimizing the risk of data breaches and privacy violations. It reduces the need for human oversight, ensuring seamless and autonomous data governance. Answer: C Explanation According to the AAIA™ Study Guide, a robust data governance framework ensures that AI systems are compliant with data protection laws, ethical standards, and internal policies. It provides controls over data quality, access, retention, and processing, all of which are essential to avoid breaches and maintain trust. “A strong data governance structure is foundational for regulatory compliance and ethical AI practices. It ensures that data privacy, integrity, and usage rights are maintained across the AI lifecycle.” While option A is an outcome of good data governance, and automation (B) may improve efficiency, the most fundamental benefit is risk reduction and compliance (C). Option D reflects a misunderstanding of governance which requires human oversight. Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: “AI Governance and Risk Management,” Subsection: “Data Governance Frameworks and Compliance” Question #:10 - [AI Governance and Risk Management] Which of the following controls MOST effectively helps to ensure an AI model is resilient against external threats? AI data set anonymization Isaca - AAIA Certs Exam 7 of 7 Pass with Valid Exam Questions Pool B. C. D. Monitoring of AI model developers Monitoring of AI access logs AI model configuration testing Answer: D Explanation Ensuring AI model resilience against external threats involves validating that the model is configured to resist attacks, such as adversarial inputs, data poisoning, or misuse. The AAIA™ Study Guide emphasizes configuration testing as a crucial control to simulate threat scenarios and assess robustness. “Model configuration testing simulates real-world threat conditions to validate model resilience. This includes testing against adversarial attacks, input manipulation, and exposure of sensitive outputs.” While access monitoring (C) and anonymization (A) reduce risks, they don’t actively validate model behavior under threat conditions. Therefore, D offers the most effective resilience measure. Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: “AI Governance and Risk Management,” Subsection: “Security and Resilience Testing for AI Models” About certsout.com certsout.com was founded in 2007. We provide latest & high quality IT / Business Certification Training Exam Questions, Study Guides, Practice Tests. We help you pass any IT / Business Certification Exams with 100% Pass Guaranteed or Full Refund. Especially Cisco, CompTIA, Citrix, EMC, HP, Oracle, VMware, Juniper, Check Point, LPI, Nortel, EXIN and so on. View list of all certification exams: All vendors We prepare state-of-the art practice tests for certification exams. 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