CSPAI Exam Introduction & CSPAI Hot Questions

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CSPAI Exam Resources & CSPAI Actual Questions & CSPAI Exam Guide

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SISA CSPAI Exam Syllabus Topics:

Topic	Details
Topic 1	AIMS and Privacy Standards: ISO 42001 and ISO 27563: This section of the exam measures skills of the AI Security Analyst and addresses international standards related to AI management systems and privacy. It reviews compliance expectations, data governance frameworks, and how these standards help align AI implementation with global privacy and security regulations.
Topic 2	Securing AI Models and Data: This section of the exam measures skills of the Cybersecurity Risk Manager and focuses on the protection of AI models and the data they consume or generate. Topics include adversarial attacks, data poisoning, model theft, and encryption techniques that help secure the AI lifecycle.
Topic 3	Using Gen AI for Improving the Security Posture: This section of the exam measures skills of the Cybersecurity Risk Manager and focuses on how Gen AI tools can strengthen an organization’s overall security posture. It includes insights on how automation, predictive analysis, and intelligent threat detection can be used to enhance cyber resilience and operational defense.

SISA Certified Security Professional in Artificial Intelligence Sample Questions (Q49-Q54):

NEW QUESTION # 49
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 tends to degrade the model's performance on the original tasks it was trained on.
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 requires significantly more data to achieve comparable performance to multi- task fine tuning.
D. Single-task fine-tuning introduces more complexity in managing different versions of the model compared to multi-task fine-tuning.

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 # 50
Which of the following is a characteristic of domain-specific Generative AI models?

A. They are trained on broad datasets covering multiple domains
B. They are only used for computer vision tasks
C. They are tailored and fine-tuned for specific fields or industries
D. They are designed to run exclusively on quantum computers

Answer: C

Explanation:
Domain-specific Generative AI models are refined versions of foundational models, adapted through fine- tuning on specialized datasets to excel in niche areas like healthcare, finance, or legal applications. This tailoring enhances precision, relevance, and efficiency by incorporating industry-specific jargon, patterns, and constraints, unlike general models that handle broad tasks but may lack depth. For example, a medical GenAI model might generate accurate diagnostic reports by focusing on clinical data, reducing errors in specialized contexts. This approach balances computational resources and performance, making them ideal for targeted deployments while maintaining the generative capabilities of larger models. Security implications include better control over sensitive domain data. Exact extract: "Domain-specific GenAI models are characterized by being tailored and fine-tuned for particular fields or industries, leveraging specialized data to achieve higher accuracy and relevance in those domains." (Reference: Cyber Security for AI by SISA Study Guide, Section on GenAI Model Types, Page 65-67).

NEW QUESTION # 51
When integrating LLMs using a Prompting Technique, what is a significant challenge in achieving consistent performance across diverse applications?

A. Overcoming the lack of transparency in understanding how the LLM interprets varying prompt structures.
B. Handling the security concerns that arise from dynamically generated prompts
C. Reducing latency in generating responses to meet real-time application requirements.
D. The need for optimizing prompt templates to ensure generalization across different contexts.

Answer: D

Explanation:
Prompting techniques in LLM integration, such as zero-shot or few-shot prompting, face challenges in consistency due to the need for meticulously optimized templates that generalize across tasks. Variations in prompt phrasing can lead to unpredictable outputs, requiring iterative engineering to balance specificity and flexibility, especially in diverse domains like legal or medical apps. This optimization involves A/B testing, semantic alignment, and incorporating chain-of-thought to enhance reasoning, but it demands expertise and time in SDLC phases. Unlike latency issues, which are hardware-related, prompt optimization directly affects performance reliability. Security overlaps, as poor prompts might expose vulnerabilities, but the core challenge is generalization. Efficient SDLC uses automated prompt tuning tools to streamline this, reducing development overhead while maintaining efficacy. Exact extract: "A significant challenge is optimizing prompt templates to ensure generalization across different contexts, crucial for consistent LLM performance in varied applications." (Reference: Cyber Security for AI by SISA Study Guide, Section on Prompting in SDLC, Page 100-103).

NEW QUESTION # 52
How does GenAI contribute to incident response in cybersecurity?

A. By focusing only on post-incident reporting.
B. By automating playbook generation and response orchestration.
C. By delaying responses to gather more data for analysis.
D. By manually reviewing each incident without AI assistance.

Answer: B

Explanation:
GenAI enhances incident response by dynamically generating customized playbooks based on threat intelligence and orchestrating automated actions like isolation or patching. It processes vast logs in real-time, correlating events to prioritize alerts and suggest optimal responses, reducing mean time to respond (MTTR).
For complex incidents, it simulates outcomes of different strategies, aiding decision-making. This automation frees analysts for strategic tasks, improving efficiency and effectiveness in containing breaches. Exact extract:
"GenAI contributes to incident response by automating playbook generation and orchestration, enhancing cybersecurity operations." (Reference: Cyber Security for AI by SISA Study Guide, Section on AI in Incident Response, Page 215-218).

NEW QUESTION # 53
What is a potential risk of LLM plugin compromise?

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

Answer: B

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 # 54
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CSPAI Exam Introduction & CSPAI Hot Questions

CSPAI Exam Resources & CSPAI Actual Questions & CSPAI Exam Guide

SISA CSPAI Exam Syllabus Topics:

SISA Certified Security Professional in Artificial Intelligence Sample Questions (Q49-Q54):

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