Question 1

You are tasked with performing a system analysis to integrate a generative LLM into an existing customer relationship management (CRM) system. The goal is to automate customer response generation while ensuring the system meets all performance and security specifications. During the analysis, you identify potential bottlenecks in the system that could affect the LLM's response time. Which strategy should you prioritize to ensure the LLM integration meets the performance specifications without compromising security?

Options :

A : Focus on enhancing the LLM’s model to handle larger datasets without adjusting the system.

B : Optimize the CRM system’s infrastructure to reduce latency and ensure faster LLM response times.

C : Limit the LLM-s capabilities to reduce its computational load and improve performance.

D : Disable security features temporarily to improve performance during peak times.

Answer: B

Question 2

You are responsible for deploying an LLM in a customer service application. The LLM should effectively manage and prioritize different types of customer queries, such as urgent complaints, general inquiries, and feedback. However, during testing, you observe that the model struggles to prioritize urgent complaints appropriately, treating them with the same priority as general inquiries. The senior team member suggests adjusting the training process to improve the model's ability to handle priorities. Which strategy is most likely to improve the model’s ability to prioritize urgent complaints?

Options :

A : Incorporate weighted loss functions to give higher importance to urgent complaints during training.

B : Add more diverse examples of general inquiries to the training dataset.

C : Use a lower learning rate during training to ensure better model stability.

D : Increase the overall training data size to improve the model-s generalization.

Answer: A

Question 3

You are working on a project that involves generating product descriptions for a large e-commerce platform. The platform sells a wide range of products, from electronics to clothing. The generative AI model currently in use produces high-quality descriptions for electronics but struggles with fashion items, often generating vague or inaccurate content. What is the most effective strategy to improve the model's performance across all product categories?

Options :

A : Switch to a smaller, less complex model to handle fashion descriptions separately

B : Use a rule-based system to generate fashion descriptions instead of AI

C : Reduce the complexity of the model to prevent overfitting

D : Fine-tune the model on a dataset specifically focused on fashion items

Answer: D

Question 4

A team is fine-tuning a pre-trained Large Language Model (LLM) to generate responses specific to their company's product offerings. After deploying the model, they observe that it occasionally generates irrelevant or off-brand content. What is the most likely cause of this issue?

Options :

A : Inadequate fine-tuning data that does not fully represent the company-s brand and products

B : The deployment environment has insufficient computational resources

C : The model-s architecture is too complex for the task

D : The pre-trained LLM was not designed for commercial use

Answer: A

Question 5

You are deploying a generative AI model that creates natural language responses in a customer service chatbot. During testing, the model occasionally generates incorrect but highly confident answers. Which of the following strategies is most appropriate to reduce this risk?

Options :

A : Increase the size of the training dataset

B : Add more layers to the model

C : Reduce the model-s temperature during inference

D : Implement an adversarial training regime

Answer: C

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