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NEW QUESTION # 46
Which of the following is an IS auditor's MOST important course of action when determining whether source data should be entered into approved generative AI tools to assist with an audit?
Answer: C
Explanation:
When using generative AI tools during audit activities, the most critical concern is the reliability and appropriateness of the information being entered and processed. According to the AAIA™ Study Guide, auditors are accountable for ensuring that audit data is valid, confidential, and that generated outputs are factual and verifiable.
"IS auditors must evaluate whether the information entered into AI tools is reliable and appropriate for the audit context. Inputting sensitive or unverified data may lead to regulatory violations or audit inaccuracies." While hallucinations (C) and privacy notices (B) are important concerns, the primary auditor responsibility is to ensure that source data is accurate and suitable. Therefore, D is the correct response.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Ethical and Legal Considerations in AI," Subsection: "Auditor Responsibility and AI Input Validation"
NEW QUESTION # 47
An organization is using a large language model (LLM) to assist in evaluating loan applications, but the training data used is known to be incomplete. Which of the following is the GREATEST associated risk?
Answer: D
Explanation:
Incomplete training dataoften leads to underrepresentation of certain applicant types, products, or scenarios.
In credit and lending, this typically translates intosystematic bias: some groups are evaluated on richer historical patterns, while others are evaluated on sparse or unrepresentative information. The greatest associated risk is thereforeunfair loan decisions(A), which can manifest as unjustified rejections, inappropriate pricing, or inconsistent risk assessments.
While delays (B), reduced satisfaction (C), or increased manual work (D) may occur, they are secondary operational issues. AAIA highlights that for financial services, the central risks includefairness, discrimination, regulatory compliance, and reputational impact. Incomplete data directly undermines fairness and can violate lending regulations and internal risk appetite.
References:
ISACA,AAIA Exam Content Outline- Domain 1: AI Governance and Risk (risk categories, including fairness and discriminatory outcomes).
ISACA AI ethics content on data completeness and representativeness in decisioning systems.
NEW QUESTION # 48
An AI tool is being implemented for a regional healthcare organization. Which of the following training methods BEST ensures the AI output does not reveal whether someone's personal data was used?
Answer: A
Explanation:
Differential privacyintroduces carefully calibrated noise during training or query responses so that it becomes mathematically difficult to infer whether any specific individual's record is included in the training set. For healthcare data-highly sensitive and subject to strict privacy laws-this technique directly supports privacy-by-design, reducing the risk that model outputs leak membership information or reconstruct personal records.
Option A uses real patient records directly and does not, by itself, mitigate inference risk. Option B (data augmentation) may expand the dataset but does not guarantee resistance to membership inference attacks.
Option D (transfer learning using public data) can help, but if any private data is used in fine-tuning, privacy risks remain. Differential privacy, as in option C, is the most appropriate control to ensure that outputs do not reveal whether particular personal data was used.
References:
ISACA,AAIA Exam Content Outline- Domain 1: Privacy and Data Governance Programs; Domain 2: Data Management Specific to AI (data confidentiality, data security).
ISACA guidance on privacy-by-design and AI risk management concepts reflected in AAIA.
NEW QUESTION # 49
A bank is deploying an AI model trained on customer transactions in a multi-cloud environment.
Which of the following BEST supports regulatory compliance regarding data security and privacy?
Answer: D
Explanation:
In a multi-cloud environment, data often traverses various geographical regions. For financial institutions, "Data residency and sovereignty" are the most critical compliance factors, ensuring that data is stored and processed within the jurisdictions required by law (e.g., GDPR or local banking acts). Failure to control where data resides can lead to severe legal penalties. While
"Privacy by Design" and "DPIA" are essential methodologies, they do not specifically address the physical and legal location of data across cloud providers. Sovereignty controls ensure the bank maintains control over its data assets and complies with specific cross-border data transfer regulations.
NEW QUESTION # 50
Which of the following is MOST important for an IS auditor to consider when collecting data for analysis by AI tools?
Answer: C
Explanation:
When collecting data for analysis by AI tools in an audit, the MOST important immediate consideration is that the data format and syntax align with the requirements of the AI tools (C).
Without correct formatting (e.g., structured fields, correct data types, consistent delimiters, proper encoding), AI tools may fail, misinterpret data, or generate unreliable results. AAIA's domain on AI in audit processes stresses data preparation, cleansing, and transformation as critical steps before applying AI analytics.
NEW QUESTION # 51
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