Online Access Free CSPAI Exam Questions

How does ISO 27563 support privacy in AI systems?

• A.By limiting AI to non-personal data only.
• B.By mandating the use of specific encryption algorithms.
• C.By focusing on performance metrics over privacy.
• D.By providing guidelines for privacy-enhancing technologies in AI.

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 is less effective in generalizing to new, unseen tasks compared to multi-task fine- tuning.
• B.Single-task fine-tuning introduces more complexity in managing different versions of the model compared 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 requires significantly more data to achieve comparable performance to multi- task fine tuning.

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

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

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

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

In a scenario where Open-Source LLMs are being used to create a virtual assistant, what would be the most effective way to ensure the assistant is continuously improving its interactions without constant retraining?

• A.Training a larger proprietary model to replace the open-source LLM
• B.Shifting the assistant to a completely rule-based system to avoid reliance on user feedback.
• C.Reducing the amount of feedback integrated to speed up deployment.
• D.Implementing reinforcement learning from human feedback (RLHF) to refine responses based on user input.