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SISA Certified Security Professional in Artificial Intelligence Sample Questions (Q26-Q31):

NEW QUESTION # 26
How does the STRIDE model adapt to assessing threats in GenAI?

A. By excluding AI-specific threats like model inversion.
B. By using it unchanged from traditional software.
C. By focusing only on hardware threats in AI systems.
D. By applying Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, and Elevation of Privilege to AI components.

Answer: D

Explanation:
The STRIDE model adapts to GenAI by evaluating threats across its categories: Spoofing (e.g., fake inputs), Tampering (e.g., data poisoning), Repudiation (e.g., untraceable generations), Information Disclosure (e.g., leakage from prompts), Denial of Service (e.g., resource exhaustion), and Elevation of Privilege (e.g., jailbreaking). This systematic threat modeling helps in designing resilient GenAI systems, incorporating AI- unique aspects like adversarial inputs. Exact extract: "STRIDE adapts to GenAI by applying its threat categories to AI components, assessing specific risks like tampering or disclosure." (Reference: Cyber Security for AI by SISA Study Guide, Section on Threat Modeling for GenAI, Page 240-243).

NEW QUESTION # 27
Fine-tuning an LLM on a single task involves adjusting model parameters to specialize in a particular domain.
What is the primary challenge associated with fine tuning for a single task compared to multi task fine tuning?

A. Single-task fine-tuning requires significantly more data to achieve comparable performance to multi- task fine tuning.
B. Single-task fine-tuning is less effective in generalizing to new, unseen tasks compared to multi-task fine- tuning.
C. Single-task fine-tuning introduces more complexity in managing different versions of the model compared to multi-task fine-tuning.
D. Single-task fine-tuning tends to degrade the model's performance on the original tasks it was trained on.

Answer: B

Explanation:
Single-task fine-tuning specializes the LLM but risks overfitting, limiting generalization to novel tasks unlike multi-task approaches that promote transfer learning across domains. This challenge requires careful regularization in SDLC to balance specificity and versatility, often needing more resources for version management. Exact extract: "Single-task fine-tuning is less effective in generalizing to new tasks compared to multi-task fine-tuning." (Reference: Cyber Security for AI by SISA Study Guide, Section on Fine-Tuning Challenges, Page 115-118).

NEW QUESTION # 28
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. Ensuring that AI systems operate safely, ethically, and without causing harm.
D. Developing AI systems with the highest accuracy regardless of data privacy concerns

Answer: C

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 # 29
What role does GenAI play in automating vulnerability scanning and remediation processes?

A. By ignoring low-priority vulnerabilities to focus on high-impact ones.
B. By compiling lists of vulnerabilities without any analysis.
C. By generating code patches and suggesting fixes based on vulnerability descriptions.
D. By increasing the frequency of manual scans to ensure thoroughness.

Answer: C

Explanation:
GenAI automates vulnerability management by analyzing scan results and generating tailored code patches or remediation strategies, accelerating the fix process and reducing human error. Using natural language processing, it interprets vulnerability reports, cross-references with known exploits, and proposes secure code alternatives, integrating seamlessly into DevSecOps pipelines. This proactive approach minimizes exposure windows and enhances system resilience against exploits. For instance, in cloud environments, GenAI can simulate patch impacts before application. This contributes to a stronger security posture by enabling rapid, accurate responses to threats. Exact extract: "GenAI automates vulnerability scanning and remediation by generating code patches and fixes, improving efficiency and security posture." (Reference: Cyber Security for AI by SISA Study Guide, Section on Automation in Vulnerability Management, Page 205-208).

NEW QUESTION # 30
Which of the following is a potential use case of Generative AI specifically tailored for CXOs (Chief Experience Officers)?

A. Automating financial transactions in blockchain networks.
B. Enhancing customer support through AI-powered chatbots that provide 24/7 assistance.
C. Developing autonomous vehicles for urban mobility solutions.
D. Conducting genetic sequencing for personalized medicine

Answer: B

Explanation:
For CXOs focused on customer experience, Generative AI excels in powering chatbots that deliver round-the- clock, personalized support, addressing queries with context-aware responses. This enhances user satisfaction by reducing wait times and tailoring interactions using predictive analytics, while integrated security measures like anomaly detection safeguard against threats like phishing. Unlike unrelated applications like autonomous vehicles or genetic sequencing, chatbots directly align with CXO goals of improving engagement and trust.
Security posture is bolstered by monitoring interactions for malicious inputs, ensuring safe AI-driven CX.
Exact extract: "Generative AI enhances customer support through AI-powered chatbots providing 24/7 assistance, tailored for CXOs to improve engagement and security." (Reference: Cyber Security for AI by SISA Study Guide, Section on GenAI for CX Enhancement, Page 75-78).

NEW QUESTION # 31
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