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| Certification Vendor: | ISACA |
|---|---|
| Exam Name: | ISACA Advanced in AI Audit |
| Exam Number: | AAIA |
| Passing Score: | 65% |
| Real Exam Qty: | 90 |
| Available Languages: | English |
| Related Certifications: | ACCA CPA FCCA CISA CIA |
| Exam Duration: | 120 minutes |
| Exam Format: | Computer-Based, Remotely Proctored, Multiple Choice |
| Sample Questions: | ISACA AAIA Sample Questions |
| Exam Way: | Online remotely proctored computer-based exam |
| Pre Condition: | Candidates must hold an active CISA certification or another qualified audit-related designation such as CIA, CPA, ACCA, FCCA, Canadian CPA, Australian CPA/FCPA, or Japanese CPA. |
| Official Syllabus URL: | https://www.isaca.org/credentialing/aaia |
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NEW QUESTION # 15
An IS auditor is reviewing the company ' s AI procedure. Which of the following would be the MOST critical gap?
Answer: C
Explanation:
AI governance requires a " Tiered Approach " based on risk. The most critical gap is the lack of " Mandatory Assessments " (such as Bias or Privacy Impact Assessments) for high-impact or critical systems. These assessments are the " Control Gates " that prevent the deployment of unsafe or unethical AI. While privacy principles (Option C) and oversight (Option D) are essential components, they are typically the outcome of the risk assessment process. Without a mandate to assess systems before they go live, the organization has no way to ensure that any of its other AI controls are being applied where they are most needed.
NEW QUESTION # 16
A healthcare AI tool recommends treatments with high success rates but significant risk. The hospital prioritizes patient safety over innovation. What is the BEST course of action?
Answer: B
Explanation:
AI systems must align with the organization's risk appetite and ethical principles, especially in healthcare where patient safety is paramount. The BEST action is to adjust the AI's parameters (A) so the recommendations reflect the hospital's conservative risk tolerance, reducing the frequency of high-risk suggestions. AAIA stresses AI governance alignment with organizational risk appetite, treatment guidelines, and ethical priorities.
NEW QUESTION # 17
An IS auditor is performing an inventory audit for a manufacturing organization. Which of the following would BEST enable the auditor to identify types of products without assistance from organizational staff?
Answer: A
Explanation:
Computer vision uses machine learning techniques to identify and classify visual data such as images or videos. In inventory audits, it can be used to recognize product types, scan barcodes, or evaluate storage conditions without human assistance.
"Computer vision is particularly effective in automated environments like manufacturing, where visual data from cameras or sensors can be processed to verify product identification and placement."
NEW QUESTION # 18
An organization is using information gathered from customer accounts to train its AI chatbot. Which of the following is the GREATEST risk associated with this practice?
Answer: A
Explanation:
The use of customer data in AI training presents a significant privacy risk, especially when the data is not properly anonymized or when consent has not been explicitly obtained. The AAIA™ Study Guide classifies the unauthorized or inadvertent disclosure of personally identifiable information (PII) as the most severe risk in such contexts.
"Training AI models on customer data without proper safeguards can result in the unintentional exposure of personal or sensitive information, leading to data breaches and compliance violations." While bias (B) and hallucinations (D) are important operational concerns, they do not pose the same regulatory and ethical severity as PII exposure. Therefore, A is the most critical risk.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Ethical and Legal Considerations in AI," Subsection: "Privacy Risks and Data Minimization"
NEW QUESTION # 19
Which of the following types of AI can use unlabeled data sets to imitate human learning behavior?
Answer: B
Explanation:
Unsupervised learningusesunlabeled datato discover patterns, structures, or groupings without explicit outcome labels. The model "learns" by identifying similarities, clusters, or latent structures within the data, somewhat analogous to how humans can notice patterns without being told the correct answer. In AAIA's fundamentals coverage, unsupervised methods (e.g., clustering, dimensionality reduction) are explicitly linked to situations wherelabels are unavailable or costly, yet insight is still needed.
Supervised learning (A) requires labeled examples (input-output pairs). Federated learning (B) describes a distributed training paradigm, not the labeling requirement itself. Reinforcement learning (C) uses feedback in the form of rewards and penalties rather than unlabeled static datasets. Therefore,unsupervised learningis the correct type that directly usesunlabeled datato learn structure.
References:
ISACA,AAIA Exam Content Outline- Domain 1: AI Models, Considerations, and Requirements (supervised, unsupervised, reinforcement learning).
ISACA AI fundamentals content on learning types and data labeling.
NEW QUESTION # 20
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