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AAIA New Questions | Latest AAIA: ISACA Advanced in AI Audit

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ISACA AAIA Exam Syllabus Topics:

Topic
Details

Topic 1

• Auditing Tools and Techniques: This section of the exam measures the skills of AI auditors and centers on auditing AI systems using appropriate tools and methods. It includes audit planning and design, sampling methodologies specific to AI, collecting audit evidence, using data analytics for quality assurance, and producing AI audit outputs and reports, including follow-up and quality control measures.

Topic 2

• AI Operations: It covers managing AI-specific data needs—including collection, quality, security, and classification—applying development lifecycle methodologies with privacy and security by design, change and incident management, testing AI solutions, identifying AI-related threats and vulnerabilities, and supervising AI deployments.

Topic 3

• AI GOVERNANCE AND RISK: It encompasses understanding different AI models and their life cycles, guiding AI strategy, defining roles and policies, managing AI-related risks, overseeing data privacy and governance, and ensuring adherence to ethical practices, standards, and regulations.

 

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ISACA Advanced in AI Audit Sample Questions (Q62-Q67):

NEW QUESTION # 62
Which of the following is the PRIMARY objective of performing adversarial testing on AI models?

• A. Validating AI incident response plans
• B. Fostering security awareness
• C. Determining key risk indicators (KRIs)
• D. Identifying control gaps

Answer: D

Explanation:
Adversarial testinginvolves simulating real-world attacks or malicious inputs against AI models (e.g., adversarial examples, poisoning, evasion) to identify how the system behaves under intentional misuse or hostile conditions. Theprimary objectiveis to discoverweaknesses and control gaps(D) in the model and its surrounding processes-such as inadequate input validation, insufficient monitoring, or missing safeguards against adversarial inputs.
While results from adversarial testing may inform incident response planning (A), KRI definition (B), or security awareness (C), those are secondary benefits. AAIA's coverage of AI threats and vulnerabilities emphasizes adversarial testing as acontrol validation and gap-identification mechanism, directly addressing AI-specific risk exposure.
References:
ISACA,AAIA Exam Content Outline- Domain 1 and Domain 2: Threats and Vulnerabilities Specific to AI; Testing Techniques for AI Solutions.
ISACA guidance on adversarial testing and AI security posture assessment.

 

NEW QUESTION # 63
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?

• A. Accuracy
• B. Recall
• C. Specificity
• D. Precision

Answer: B

 

NEW QUESTION # 64
Which of the following should be an IS auditor's GREATEST concern when using a predictive AI tool to analyze data abnormalities?

• A. The speed at which the AI tool processes large data sets
• B. The cost of implementing and maintaining the AI tool for data audit purposes
• C. The false positives or false negatives generated by the AI tool
• D. The ease of integrating the AI tool with existing data audit software

Answer: C

Explanation:
For apredictive AI tool analyzing abnormalities, the GREATEST concern is therate and impact of false positives and false negatives(A). False positives can lead to unnecessary investigation, while false negatives mean true issues (e.g., fraud, control failures) remain undetected. From an assurance perspective, false negatives are especially critical because they directly undermine audit objectives. AAIA underscores that key performance metrics (e.g., precision, recall) and error trade-offs are essential in evaluating AI tools used in audit.
Integration ease (B), speed (C), and cost (D) are important practical considerations but are secondary to whether the toolaccurately identifies or misses significant anomalies. Therefore, error behavior-false positives and false negatives-represents the primary risk to audit quality.
References:
ISACA,AAIA Exam Content Outline- Domain 3: AI in Audit Processes; Domain 2: AI Operations (model performance metrics and risk).
ISACA analytics guidance on evaluating AI tools using precision, recall, and error analysis in audit contexts.

 

NEW QUESTION # 65
From a data appropriateness and bias perspective, which of the following should be of GREATEST concern when reviewing an AI model used in a credit scoring system?

• A. The model uses postal codes as a primary factor in determining creditworthiness.
• B. The model incorporates the applicant's loan history to assess spending habits.
• C. The model utilizes historical credit data to predict future credit behavior.
• D. The model considers the applicant's income level as a key factor in the credit decision.

Answer: A

Explanation:
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.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Governance and Risk Management," Subsection: "Bias and Fairness in Financial AI Models"

 

NEW QUESTION # 66
Which of the following is the GREATEST risk associated with using AI in audit planning?

• A. Limited knowledge
• B. Scope creep
• C. Increased planning costs
• D. Incomplete data

Answer: D

 

NEW QUESTION # 67
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