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| Certification Vendor: | ISACA |
|---|---|
| Exam Name: | ISACA Advanced in AI Audit (AAIA) Exam |
| Exam Number: | AAIA |
| Real Exam Qty: | 55 |
| Passing Score: | 65% |
| Exam Price: | USD 459 (member), USD 599 (non-member) |
| Related Certifications: | CPA CISA CIA |
| Exam Duration: | 120 minutes |
| Certificate Validity Period: | Not publicly specified (requires ongoing CPE maintenance after certification) |
| Available Languages: | English |
| Exam Format: | Multiple-choice, Closed-book, Remote proctoring or test center, Computer-based |
| Recommended Training: | ISACA AI Audit Training Resources Official AAIA Training Course Providers |
| 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 # 308
Which of the following is the MOST significant benefit of performing frequent AI model testing and retraining?
Answer: A
Explanation:
AI models are not "static" systems; they are reflections of the data they were trained on. In dynamic environments, data patterns change constantly (e.g., market trends or new fraud tactics). Frequent testing and retraining ensure the model is "updated with current data," which prevents performance degradation and "data drift."
NEW QUESTION # 309
An organization implements a neural network-based AI system for credit scoring that processes extensive input data from multiple sources. Which of the following represents the GREATEST risk if input validation and anomaly detection controls are inadequate?
Answer: A
Explanation:
In neural network-based credit scoring, the model's complexity often obscures the relationship between specific inputs and final outcomes. If input validation and anomaly detection are weak,
"biased inputs" or "noise" from various sources can seep into the training and inference pipelines.
According to the ISACA AAIATM framework, these biased inputs can cause the model to learn discriminatory patterns (e.g., correlations between ethnicity and creditworthiness) that are hidden within large datasets. This leads to unethical and potentially illegal credit decisions.
NEW QUESTION # 310
Which of the following is the BEST approach to mitigate the risk of AI model degradation?
Answer: D
Explanation:
"Model degradation" (or model decay) happens when a model's performance slowly worsens because the real-world environment has changed since its last training. The most effective safeguard is "Periodic human reviews." Humans can identify "contextual shifts" or "common- sense errors" that automated monitoring might miss. This "Human-in-the-Loop" (HITL) control ensures that the AI's decisions remain grounded, justifiable, and accurate over time.
NEW QUESTION # 311
An organization deploys an AI-based image recognition system that is vulnerable to evasion attacks. Which of the following approaches BEST helps to ensure the system mitigates these evasion attempts?
Answer: D
Explanation:
Evasion attacks occur when an attacker modifies input data (such as adding subtle noise to an image) to trick a model into misclassification. The AAIATM manual identifies "Adversarial Training" as a primary defense, where the model is intentionally exposed to adversarial examples during the training phase to improve its robustness and resilience. This allows the model to learn the patterns associated with malicious inputs.
NEW QUESTION # 312
Which of the following evaluation criteria is the HIGHEST priority when auditing an organization's management of AI vendors?
Answer: D
Explanation:
When outsourcing AI, the organization retains accountability for the model's decisions. The highest priority for an auditor is ensuring "Interpretability"--the ability of the vendor to provide tools or documentation that explain the model's rationale. Without this, the organization cannot satisfy regulatory requirements for transparency (e.g., GDPR Right to Explanation) or verify that the vendor's model is not biased. While SLOs (Option B) are important for operational uptime, they do not mitigate the ethical or legal risks associated with "Black Box" algorithms provided by third parties.
NEW QUESTION # 313
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