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| Certification Vendor: | ISACA |
|---|---|
| Exam Name: | ISACA Advanced in AI Audit (AAIA) Exam |
| Exam Number: | AAIA |
| Passing Score: | 65% |
| Real Exam Qty: | 55 |
| Related Certifications: | CISA CPA CIA |
| Exam Duration: | 120 minutes |
| Certificate Validity Period: | Not publicly specified (requires ongoing CPE maintenance after certification) |
| Exam Format: | Closed-book, Computer-based, Remote proctoring or test center, Multiple-choice |
| Exam Price: | USD 459 (member), USD 599 (non-member) |
| Available Languages: | English |
| Recommended Training: | Official AAIA Training Course Providers ISACA AI Audit Training Resources |
| Exam Registration: | ISACA AAIA Certification Page |
| Sample Questions: | ISACA AAIA Sample Questions |
| Exam Way: | Computer-based exam delivered via PSI test centers or remote proctoring |
| Pre Condition: | Must hold an active CISA, CIA, CPA, or equivalent ISACA-approved advanced auditing certification |
| Official Syllabus URL: | https://www.isaca.org/credentialing/aaia |
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NEW QUESTION # 223
Which of the following is the MOST important course of action for an organization prior to allowing end users to utilize an AI tool?
Answer: B
Explanation:
An AI usage policy sets the foundation for safe, ethical, and effective AI deployment. According to the AAIATM Study Guide, having an AI policy in place ensures that users understand acceptable behaviors, limitations, and responsibilities when interacting with AI tools.
"AI acceptable use policies promote governance by clearly outlining the dos and don'ts of AI interaction, preventing misuse and aligning user activity with organizational values and compliance standards."
NEW QUESTION # 224
An IS auditor learns that an organization uses AI facial recognition technology for security purposes. Which of the following is the GREATEST ethical concern with this practice?
Answer: D
Explanation:
According to the ISACA AAIA™ Study Guide, facial recognition systems are prone to significant ethical risks related to fairness and bias. These systems often exhibit higher error rates for specific demographic groups-particularly based on race, gender, and age-due to non-representative training datasets. For an IS auditor, this represents a major compliance and reputational risk, as biased outcomes can lead to discriminatory access or false accusations. While data security (Option A) and the lack of standards (Option C) are relevant, the fundamental ethical imperative in AI governance is ensuring that biometric applications do not disproportionately disadvantage protected groups.
NEW QUESTION # 225
An auditor discovers that a model's training data was collected five years ago and the business environment has changed significantly. What risk does this MOST directly represent?
Answer: A
Explanation:
Concept drift occurs when the statistical relationship between inputs and the target variable changes over time, often due to shifting real-world conditions -- distinct from data drift, which concerns the input distribution alone.
NEW QUESTION # 226
Which of the following is MOST important to perform when testing an AI model for security?
Answer: A
Explanation:
AI security testing must address both traditional IT vulnerabilities and AI-specific threats.
According to the AAIATM framework, "Vulnerability Assessments" are essential for identifying weaknesses in the AI pipeline, including insecure APIs, lack of input sanitization, and susceptibility to adversarial attacks. This assessment helps auditors determine if the system can resist exploitation, such as prompt injection or data poisoning.
NEW QUESTION # 227
Which of the following techniques is BEST to use when there is a limited dataset of detailed images available to train a convolutional neural network (CNN) model?
Answer: D
Explanation:
When data is scarce, training a deep CNN from scratch is likely to result in overfitting. "Transfer learning" allows an organization to take a model previously trained on a massive dataset (like ImageNet) and "fine-tune" it for their specific, smaller dataset. The model already knows how to recognize basic shapes and textures, so it requires much less data to learn specific new categories. This is a common and highly effective practice in medical imaging or specialized manufacturing audits.
NEW QUESTION # 228
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