CSPAI 시험 - SISA실제시험문제와 답 - 50문항

Question No : 1

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

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

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Question No : 2

In a financial technology company aiming to implement a specialized AI solution, which approach would most effectively leverage existing AI models to address specific industry needs while maintaining efficiency and accuracy?

A.Adopting a Foundation Model as the base and fine-tuning it with domain-specific financial data to enhance its capabilities for forecasting and risk assessment.
B.Integrating multiple separate Domain-Specific GenAI models for various financial functions without using a foundational model for consistency
C.Building a new, from scratch Domain-Specific GenAI model for financial tasks without leveraging preexisting models.
D.Using a general Large Language Model (LLM) without adaptation, relying solely on its broad capabilities to handle financial tasks.

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Question No : 3

In ISO 42001, what is required for AI risk treatment?

A.Identifying, analyzing, and evaluating AI-specific risks with treatment plans.
B.Ignoring risks below a certain threshold.
C.Delegating all risk management to external auditors.
D.Focusing only on post-deployment risks.

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Question No : 4

In assessing GenAI supply chain risks, what is a critical consideration?

A.Evaluating third-party components for embedded vulnerabilities.
B.Ignoring open-source dependencies to reduce complexity.
C.Focusing only on internal development risks.
D.Assuming all vendors comply with standards automatically.

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Question No : 5

In a Retrieval-Augmented Generation (RAG) system, which key step is crucial for ensuring that the generated response is contextually accurate and relevant to the user's question?

A.Leveraging a diverse set of data sources to enrich the response with varied perspectives
B.Integrating advanced search algorithms to ensure the retrieval of highly relevant documents for context.
C.Utilizing feedback mechanisms to continuously improve the relevance of responses based on user interactions.
D.Retrieving relevant information from the vector database before generating a response

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Question No : 6

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

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Question No : 7

An organization is evaluating the risks associated with publishing poisoned datasets.
What could be a significant consequence of using such datasets in training?

A.Increased model efficiency in processing and generation tasks.
B.Enhanced model adaptability to diverse data types.
C.Compromised model integrity and reliability leading to inaccurate or biased outputs
D.Improved model performance due to higher data volume.

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Question No : 8

In line with the US Executive Order on AI, a company's AI application has encountered a security vulnerability.
What should be prioritized to align with the order's expectations?

A.Implementing a rapid response to address and remediate the vulnerability, followed by a review of security practices.
B.Immediate public disclosure of the vulnerability.
C.Halting all AI projects until a full investigation is complete.
D.Ignoring the vulnerability if it does not affect core functionalities.

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Question No : 9

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

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

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Question No : 10

What is a primary step in the risk assessment model for GenAI data privacy?

A.Ignoring data sources to speed up assessment.
B.Conducting data flow mapping to identify privacy risks.
C.Limiting assessment to model outputs only.
D.Relying on vendor assurances without verification.

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Question No : 11

In the Retrieval-Augmented Generation (RAG) framework, which of the following is the most critical factor for improving factual consistency in generated outputs?

A.Fine-tuning the generative model with synthetic datasets generated from the retrieved documents
B.Utilising an ensemble of multiple LLMs to cross-check the generated outputs.
C.Implementing a redundancy check by comparing the outputs from different retrieval modules.
D.Tuning the retrieval model to prioritize documents with the highest semantic similarity

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Question No : 12

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 generating code patches and suggesting fixes based on vulnerability descriptions.
C.By increasing the frequency of manual scans to ensure thoroughness.
D.By compiling lists of vulnerabilities without any analysis.

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Question No : 13

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 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 tends to degrade the model's performance on the original tasks it was trained on.

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Question No : 14

In transformer models, how does the attention mechanism improve model performance compared to RNNs?

A.By enabling the model to attend to both nearby and distant words simultaneously, improving its understanding of long-term dependencies
B.By processing each input independently, ensuring the model captures all aspects of the sequence equally.
C.By enhancing the model's ability to process data in parallel, ensuring faster training without compromising context.
D.By dynamically assigning importance to every word in the sequence, enabling the model to focus on relevant parts of the input.

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Question No : 15

What is a potential risk of LLM plugin compromise?

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

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SISA CSPAI 시험