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| Section | Objectives |
|---|---|
| AI Governance and Risk | - AI Governance Frameworks
|
| AI Operations | - AI Data Management
|
| AI Auditing Tools and Techniques | - Audit Testing and Reporting
|
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NEW QUESTION # 192
Which of the following techniques BEST enhances the security and robustness of AI models against adversarial attacks?
Answer: D
Explanation:
Defensive distillation is a specialized technique designed to make an AI model more robust against adversarial " perturbations " -small changes to input data intended to trick the model. It works by training a smaller model to mimic the probability distributions of a larger, pre-trained model, which effectively " smooths " the decision boundaries and makes it harder for attackers to find exploitable gaps. While access reviews and awareness training are standard security controls, they do not address the mathematical vulnerabilities inherent in ML algorithms. Defensive distillation specifically targets the model ' s resilience to technical exploitation.
NEW QUESTION # 193
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?
Answer: D
Explanation:
In healthcare AI applications, protecting patient data is critical. The AAIATM 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."
NEW QUESTION # 194
Which of the following is the PRIMARY benefit of implementing a robust data governance framework specific to AI solutions in an organization?
Answer: A
Explanation:
According to the AAIA™ Study Guide, a robust data governance framework ensures that AI systems are compliant with data protection laws, ethical standards, and internal policies. It provides controls over data quality, access, retention, and processing, all of which are essential to avoid breaches and maintain trust.
"A strong data governance structure is foundational for regulatory compliance and ethical AI practices. It ensures that data privacy, integrity, and usage rights are maintained across the AI lifecycle." While option A is an outcome of good data governance, and automation (B) may improve efficiency, the most fundamental benefit is risk reduction and compliance (C). Option D reflects a misunderstanding of governance which requires human oversight.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Governance and Risk Management," Subsection: "Data Governance Frameworks and Compliance"
NEW QUESTION # 195
During an audit of a bank's AI credit scoring system, an IS auditor discovers that applicants were not informed about automated decision-making. Which of the following should the auditor do FIRST?
Answer: D
Explanation:
Transparency is a fundamental legal and ethical requirement for AI systems, particularly under regulations like GDPR, which mandate that data subjects be informed of automated decision- making. If an auditor finds that applicants were not informed, the immediate "First" step is to
"Evaluate transparency controls" to determine why the notification process failed and to assess the scope of the non-compliance. This includes reviewing user agreements, privacy notices, and communication procedures.
NEW QUESTION # 196
Which of the following techniques is MOST appropriate for model hyperparameter tuning?
Answer: C
Explanation:
Hyperparameter tuning is the process of finding the optimal settings (like learning rate or depth) for an AI model. " Bayesian optimization " is an efficient, automated technique that builds a probability model of the objective function to select the most promising hyperparameters to test next. Unlike simple " grid search, " it saves significant time and computational cost. F1 score analysis is a way to measure the result of the tuning, but it is not the tuning method itself. Bayesian optimization is favored in advanced AI development for its ability to find optimal configurations in complex, multi-dimensional spaces.
NEW QUESTION # 197
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