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SISA Certified Security Professional in Artificial Intelligence Sample Questions (Q32-Q37):

NEW QUESTION # 32
During the development of AI technologies, how did the shift from rule-based systems to machine learning models impact the efficiency of automated tasks?

A. Increased system complexity and the requirement for specialized knowledge,
B. Improved scalability and performance in handling diverse and evolving data.
C. Enabled more dynamic decision-making and adaptability with minimal manual intervention
D. Enhanced the precision and relevance of automated outputs with reduced manual tuning.

Answer: C

Explanation:
The transition from rigid rule-based systems, which rely on predefined logic and struggle with variability, to machine learning models introduced data-driven learning, allowing systems to adapt dynamically to new patterns with less human oversight. This shift boosted efficiency in automated tasks by enabling real-time adjustments, such as in spam detection where ML models evolve with threats, unlike static rules. It minimized manual rule updates, fostering scalability and handling complex, unstructured data effectively. However, it introduced challenges like interpretability needs. In GenAI evolution, this paved the way for advanced models like Transformers, impacting sectors by automating nuanced decisions. Exact extract: "The shift enabled more dynamic decision-making and adaptability with minimal manual intervention, significantly improving the efficiency of automated tasks." (Reference: Cyber Security for AI by SISA Study Guide, Section on AI Evolution and Impacts, Page 20-23).

NEW QUESTION # 33
How do ISO 42001 and ISO 27563 integrate for comprehensive AI governance?

A. By focusing ISO 42001 on privacy and ISO 27563 on management.
B. By replacing each other in different organizational contexts.
C. By combining AI management with privacy standards to address both operational and data protection needs.
D. By applying only to public sector AI systems.

Answer: C

Explanation:
The integration of ISO 42001 and ISO 27563 provides a holistic framework: 42001 for overall AI governance and risk management, complemented by 27563's privacy-specific tools, ensuring balanced, compliant AI deployments that protect data while optimizing operations. Exact extract: "ISO 42001 and ISO 27563 integrate to combine AI management with privacy standards for comprehensive governance." (Reference:
Cyber Security for AI by SISA Study Guide, Section on Integrating ISO Standards, Page 280-283).

NEW QUESTION # 34
For effective AI risk management, which measure is crucial when dealing with penetration testing and supply chain security?

A. Conduct comprehensive penetration testing and continuously evaluate both internal systems and third- party components in the supply chain.
B. Perform occasional penetration testing and only address vulnerabilities in the internal network.
C. Prioritize external audits over internal penetration testing to assess supply chain security.
D. Implement penetration testing only for high-risk components and ignore less critical ones

Answer: A

Explanation:
Effective AI risk management requires comprehensive penetration testing and continuous evaluation of both internal and third-party supply chain components to identify vulnerabilities like backdoors or weak APIs. This holistic approach, aligned with SISA risk models, ensures robust security across the AI ecosystem, unlike limited or external-only testing. Exact extract: "Comprehensive penetration testing and continuous evaluation of internal and third-party components are crucial for AI risk management." (Reference: Cyber Security for AI by SISA Study Guide, Section on AI Risk Assessment Models, Page 180-183).

NEW QUESTION # 35
What does the OCTAVE model emphasize in GenAI risk assessment?

A. Operational Critical Threat, Asset, and Vulnerability Evaluation focused on organizational risks.
B. Solely technical vulnerabilities in AI models.
C. Exclusion of stakeholder input in assessments.
D. Short-term tactical responses over strategic planning.

Answer: A

Explanation:
OCTAVE adapts to GenAI by emphasizing organizational risk perspectives, identifying critical assets like models and data, evaluating threats, and prioritizing mitigations through stakeholder collaboration. It fosters a strategic, enterprise-wide approach to AI risks, integrating business impacts. Exact extract: "OCTAVE emphasizes operational critical threat, asset, and vulnerability evaluation in GenAI risk assessment." (Reference: Cyber Security for AI by SISA Study Guide, Section on OCTAVE for AI, Page 255-258).

NEW QUESTION # 36
When dealing with the risk of data leakage in LLMs, which of the following actions is most effective in mitigating this issue?

A. Relying solely on model obfuscation techniques
B. Applying rigorous access controls and anonymization techniques to training data.
C. Using larger datasets to overshadow sensitive information.
D. Allowing unrestricted access to training data.

Answer: B

Explanation:
Data leakage in LLMs occurs when sensitive information from training data is inadvertently revealed in outputs, posing privacy risks. Effective mitigation involves strict access controls, such as role-based permissions, and anonymization methods like differential privacy or tokenization to obscure personal data.
These measures prevent extraction attacks while maintaining model utility. Regular audits and data minimization further strengthen defenses. Unlike obfuscation alone, which may not fully protect, combined controls ensure compliance with regulations like GDPR. Exact extract: "Applying rigorous access controls and anonymization techniques to training data is most effective in mitigating data leakage risks in LLMs." (Reference: Cyber Security for AI by SISA Study Guide, Section on Data Security in AI Models, Page 130-
133).

NEW QUESTION # 37
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