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| Certification Vendor: | ISACA |
|---|---|
| Exam Name: | ISACA Advanced in AI Audit |
| Exam Number: | AAIA |
| Passing Score: | 65% |
| Real Exam Qty: | 90 |
| Related Certifications: | FCCA CPA CISA ACCA CIA |
| Available Languages: | English |
| Exam Duration: | 120 minutes |
| Exam Format: | Multiple Choice, Remotely Proctored, Computer-Based |
| 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 |
努力する人生と努力しない人生は全然違いますなので、あなたはのんびりした生活だけを楽しみしていき、更なる進歩を求めるのではないか？スマートを一方に置いて、我々ISACAのAAIA試験問題集をピックアップします。弊社のAAIA試験問題集によって、あなたの心と精神の満足度を向上させながら、勉強した後AAIA試験資格認定書を受け取って努力する人生はすばらしいことであると認識られます。
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質問 # 107
Which of the following is the MOST frequent cause of generative AI model hallucinations?
正解：B
解説：
Hallucinations--instances where a generative model produces factual errors or nonsensical information with high confidence--are primarily caused by "Inadequate data quality" in the training set. If the model is trained on data that is contradictory, incomplete, or contains "noise" (incorrect facts), it fails to learn accurate semantic relationships. The ISACA AAIATM manual highlights that
"Data Cleaning" and "Provenance" are essential to mitigate this.
質問 # 108
An organization's AI model used in the recruitment screening process was found to favor one gender of candidates over others. It was noted that the AI model was trained on recruitment records where the majority of new hires were male. Which bias would this BEST demonstrate?
正解：D
解説：
"Historical Bias" occurs when the training data reflects existing social prejudices or past inequalities. If an organization's historical hiring practices favored males, an AI model trained on those records will "learn" that being male is a characteristic of a successful candidate. This automates and perpetuates past discrimination into future decisions. The AAIATM manual emphasizes that auditors must evaluate training data for historical bias to ensure fairness. Unlike confirmation bias (which involves seeking data that supports existing beliefs) or label bias (which involves incorrect classification), historical bias is a systemic issue inherent in the data collection from a flawed real-world environment.
質問 # 109
A healthcare organization uses patient data to train an AI model for early disease detection. Which of the following practices provides the BEST assurance that personal data is secure and its integrity is maintained?
正解：D
解説：
In healthcare AI applications, protecting patient data is critical. The AAIA™ Study Guide identifies anonymization as one of the most effective strategies to preserve privacy and maintain data integrity. When combined with quality checks, it ensures data accuracy and compliance with health data protection regulations (e.g., HIPAA, GDPR).
"Anonymizing sensitive data removes identifying attributes, significantly reducing risk if data is accessed or leaked. Ongoing data quality checks ensure the integrity and utility of the anonymized dataset." While encryption (A) and access controls (C) are necessary technical safeguards, D provides the strongest dual assurance of privacy and accuracy. Option B focuses on model management rather than data security.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Ethical and Legal Considerations in AI," Subsection: "Privacy and Security of Sensitive AI Data"
質問 # 110
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?
正解：A
解説：
Incomplete training data often leads to underrepresentation of certain applicant types, products, or scenarios. In credit and lending, this typically translates into systematic bias: some groups are evaluated on richer historical patterns, while others are evaluated on sparse or unrepresentative information. The greatest associated risk is therefore unfair loan decisions (A), which can manifest as unjustified rejections, inappropriate pricing, or inconsistent risk assessments.
質問 # 111
Which of the following is the GREATEST challenge facing IS auditors evaluating the explainability of generative AI models?
正解：B
解説：
The greatest challenge for IS auditors in evaluating the explainability of generative AI models is the changing nature of algorithms as AI continues to learn (option D). Generative AI models, especially those using advanced techniques like deep learning and reinforcement learning, often employ continuous or dynamic learning, which results in models that evolve over time. This adaptability can significantly hinder explainability because the logic, parameters, or decision pathways may shift with ongoing retraining or real- time learning.
The ISACA Advanced in AI Audit™ (AAIA™) Study Guide stresses that: "Continually learning AI systems present unique audit challenges, as their internal representations and reasoning can change after deployment, making it difficult to fully capture and explain the rationale for outputs at any given point." Other options, such as bias in input data or computational performance, are significant but do not pose as fundamental a challenge to explainability as a model whose internal workings can dynamically change.
Reference:ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Explainability and Dynamic Models"
質問 # 112
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