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SISA Certified Security Professional in Artificial Intelligence Sample Questions (Q28-Q33):

NEW QUESTION # 28
What is a key concept behind developing a Generative AI (GenAI) Language Model (LLM)?

A. Data-driven learning with large-scale datasets
B. Operating only in supervised environments
C. Human intervention for every decision
D. Rule-based programming

Answer: A

Explanation:
GenAI LLMs rely on data-driven learning, leveraging vast datasets to model language patterns, semantics, and contexts through unsupervised or semi-supervised methods. This enables scalability and adaptability, unlike rule-based systems or human-dependent approaches. Large datasets drive generalization, though they introduce security challenges like data quality control. Exact extract: "A key concept of GenAI LLMs is data- driven learning with large-scale datasets, enabling robust language modeling." (Reference: Cyber Security for AI by SISA Study Guide, Section on GenAI Development Principles, Page 60-63).

NEW QUESTION # 29
What does the OCTAVE model emphasize in GenAI risk assessment?

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

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 # 30
Which of the following describes the scenario where an LLM is embedded 'As-is' into an application frame?

A. Using the LLM solely for backend data processing, while the application handles all user interactions.
B. Replacing the LLM with a more specialized model tailored to the application's needs.
C. Customizing the LLM to fit specific application requirements and workflows before integration.
D. Integrating the LLM into the application without modifications, using its out-of-the-box capabilities directly within the application.

Answer: D

Explanation:
Embedding an LLM 'as-is' means direct integration of the pretrained model into the app framework without alterations, relying on its inherent capabilities for tasks like text generation, simplifying SDLC by avoiding customization overhead. This is suitable for general-purpose apps but may lack optimization for specifics, contrasting with tailored approaches. It accelerates deployment while posing risks like unmitigated biases, necessitating post-integration safeguards. Exact extract: "It describes integrating the LLM without modifications, using out-of-the-box capabilities directly in the application." (Reference: Cyber Security for AI by SISA Study Guide, Section on LLM Integration Methods, Page 110-113).

NEW QUESTION # 31
What is a potential risk of LLM plugin compromise?

A. Reduced model training time
B. Better integration with third-party tools
C. Unauthorized access to sensitive information through compromised plugins
D. Improved model accuracy

Answer: C

Explanation:
LLM plugin compromises occur when extensions or integrations, like API-connected tools in systems such as ChatGPT plugins, are exploited, leading to unauthorized data access or injection attacks. Attackers might hijack plugins to leak user queries, training data, or system prompts, breaching privacy and enabling further escalations like lateral movement in networks. This risk is amplified in open ecosystems where plugins handle sensitive operations, necessitating vetting, sandboxing, and encryption. Unlike benefits like accuracy gains, compromises erode trust and invite regulatory penalties. Mitigation strategies include regular vulnerability scans, least-privilege access, and monitoring for anomalous plugin behavior. In AI security, this highlights the need for robust plugin architectures to prevent cascade failures. Exact extract: "A potential risk of LLM plugin compromise is unauthorized access to sensitive information, which can lead to data breaches and privacy violations." (Reference: Cyber Security for AI by SISA Study Guide, Section on Plugin Security in LLMs, Page 155-158).

NEW QUESTION # 32
Which of the following is a primary goal of enforcing Responsible AI standards and regulations in the development and deployment of LLMs?

A. Focusing solely on improving the speed and scalability of AI systems
B. Maximizing model performance while minimizing computational costs.
C. Developing AI systems with the highest accuracy regardless of data privacy concerns
D. Ensuring that AI systems operate safely, ethically, and without causing harm.

Answer: D

Explanation:
Responsible AI standards, including ISO 42001 for AI management systems, aim to promote ethical development, ensuring safety, fairness, and harm prevention in LLM deployments. This encompasses bias mitigation, transparency, and accountability, aligning with societal values. Regulations like the EU AI Act reinforce this by categorizing risks and mandating safeguards. The goal transcends performance to foster trust and sustainability, addressing issues like discrimination or misuse. Exact extract: "The primary goal is to ensure AI systems operate safely, ethically, and without causing harm, as outlined in standards like ISO
42001." (Reference: Cyber Security for AI by SISA Study Guide, Section on Responsible AI and ISO Standards, Page 150-153).

NEW QUESTION # 33
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