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CSPAI Complete Exam Dumps & CSPAI Exam Vce

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SISA Certified Security Professional in Artificial Intelligence Sample Questions (Q48-Q53):

NEW QUESTION # 48
When deploying LLMs in production, what is a common strategy for parameter-efficient fine-tuning?

A. Training the model from scratch on the target task to achieve optimal performance.
B. Freezing the majority of model parameters and only updating a small subset relevant to the task
C. Using external reinforcement learning to adjust the model's parameters dynamically.
D. Implementing multiple independent models for each specific task instead of fine tuning a single model

Answer: B

Explanation:
Parameter-efficient fine-tuning (PEFT) strategies, like LoRA or adapters, freeze most pretrained parameters and train only lightweight modules, reducing computational costs while adapting to new tasks. This preserves general knowledge, prevents catastrophic forgetting, and enables quick deployments in resource-constrained settings. For LLMs, it's crucial for efficiency in production, allowing specialization without retraining billions of parameters. Security-wise, it minimizes exposure to new data risks. Exact extract: "A common strategy is freezing the majority of model parameters and updating only a small task-relevant subset, ensuring efficiency in fine-tuning for production deployment." (Reference: Cyber Security for AI by SISA Study Guide, Section on Efficient Fine-Tuning in SDLC, Page 90-92).

NEW QUESTION # 49
What is the main objective of ISO 42001 in AI management systems?

A. To provide guidelines only for small-scale AI projects.
B. To establish requirements for an AI management system within organizations.
C. To focus solely on technical specifications for AI algorithms.
D. To regulate hardware used in AI deployments.

Answer: B

Explanation:
ISO 42001 outlines a framework for organizations to manage AI responsibly, covering risk assessment, governance, and continual improvement. It ensures alignment with ethical principles, promoting trustworthy AI through structured processes. Applicable across sectors, it integrates with existing management systems like ISO 27001. Exact extract: "The main objective of ISO 42001 is to establish requirements for an AI management system in organizations." (Reference: Cyber Security for AI by SISA Study Guide, Section on ISO 42001 Overview, Page 260-263).

NEW QUESTION # 50
In the context of a supply chain attack involving machine learning, which of the following is a critical component that attackers may target?

A. The underlying ML model and its training data.
B. The user interface of the AI application
C. The marketing materials associated with the AI product
D. The physical hardware running the AI system

Answer: A

Explanation:
Supply chain attacks in ML exploit vulnerabilities in the ecosystem, with the core ML model and training data being prime targets due to their foundational role in system behavior. Attackers might inject backdoors into pretrained models via compromised libraries (e.g., PyTorch or TensorFlow packages) or poison datasets during sourcing, leading to manipulated outputs or data exfiltration. This is more critical than targeting UI or hardware, as model/data compromises persist across deployments, enabling stealthy, long-term exploits like trojan attacks. Mitigation includes verifying model provenance, using secure repositories, and conducting integrity checks with hashing or digital signatures. In SISA guidelines, emphasis is on end-to-end supply chain auditing to prevent such intrusions, which could result in biased decisions or security breaches in applications like recommendation systems. Protecting these components ensures model reliability and data confidentiality, integral to AI security posture. Exact extract: "In supply chain attacks on machine learning, attackers critically target the underlying ML model and its training data to introduce persistent vulnerabilities." (Reference: Cyber Security for AI by SISA Study Guide, Section on Supply Chain Risks in AI, Page 145-148).

NEW QUESTION # 51
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 # 52
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 introduces more complexity in managing different versions of the model compared to multi-task fine-tuning.
B. Single-task fine-tuning requires significantly more data to achieve comparable performance to multi- task fine tuning.
C. Single-task fine-tuning tends to degrade the model's performance on the original tasks it was trained on.
D. Single-task fine-tuning is less effective in generalizing to new, unseen tasks compared to multi-task fine- tuning.

Answer: D

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 # 53
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