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| Section | Objectives |
|---|---|
| Topic 1: AI Auditing Tools and Techniques | - AI Audit Planning
|
| Topic 2: AI Operations | - AI System Lifecycle
|
| Topic 3: AI Governance and Risk | - AI Security and Controls
|
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NEW QUESTION # 71
Which of the following techniques BEST supports machine learning (ML) training in sentiment analysis?
Answer: C
Explanation:
Sentiment analysis is a classification task where text is categorized (e.g., positive, negative, or neutral).
Logistic regression is a fundamental and highly effective statistical method for binary or multi-class classification, making it a standard choice for sentiment models. It estimates the probability of an input belonging to a specific class. While more complex architectures like Transformers (LLMs) are now common, logistic regression remains a benchmark for its simplicity and transparency in audit contexts. Image recognition and ANOVA are not relevant to the linguistic processing required for sentiment analysis, and the log-rank test is typically used in survival analysis rather than NLP classification.
NEW QUESTION # 72
An organization uses an AI image generation platform to create promotional materials. An IS auditor identifies that the platform includes copyrighted images in its training data. Which of the following is the auditor's BEST recommendation to address this issue?
Answer: A
Explanation:
Ensuring that AI tools are trained on properly licensed and documented data sets is critical to avoiding copyright infringement and legal exposure. The AAIA™ Study Guide emphasizes using platforms with certified and traceable training data to meet ethical and legal standards.
"Organizations must verify the provenance and licensing of data used to train AI systems. Platforms that certify data sources reduce the risk of using protected intellectual property without consent." Manual review (A) is resource-intensive and may not detect embedded copyright violations. Labeling (C) is not sufficient for legal protection. Suspension (D) may be excessive without first attempting remediation.
Thus, B is the most strategic and effective recommendation.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Ethical and Legal Considerations in AI," Subsection: "Intellectual Property and Data Licensing in AI Systems"
NEW QUESTION # 73
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: A
Explanation:
According to the ISACA AAIATM 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.
NEW QUESTION # 74
Which of the following is the MOST frequent cause of generative AI model hallucinations?
Answer: B
Explanation:
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 AAIA™ manual highlights that " Data Cleaning " and " Provenance " are essential to mitigate this. While weak controls (Option C) or poor change management (Option D) can lead to other risks, the mathematical root of the hallucination itself is typically found in the underlying quality and accuracy of the training data.
NEW QUESTION # 75
Which of the following do supervised AI learning models PRIMARILY use to train algorithms?
Answer: C
NEW QUESTION # 76
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