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 tasked with deploying a Large Language Model (LLM) to generate product recommendations for an e-commerce platform. After deployment, you notice that the model tends to recommend high-priced items disproportionately, even when the user has shown a preference for budget-friendly options. The senior team member suggests revisiting the training data and model configuration to ensure the recommendations are more aligned with user preferences. What adjustment is most likely to correct the model’s bias toward recommending high-priced items?

Options :

A : Increase the size of the dataset with examples of budget-friendly items.

B : Incorporate user preference features directly into the recommendation algorithm.

C : Use a balanced dataset where high-priced and budget-friendly items are equally represented.

D : Reduce the learning rate during training to prevent the model from overfitting to high-priced items.

Answer: B

Question 3

You are developing a generative AI model that will be used in healthcare to generate personalized treatment plans for patients based on their medical history and symptoms. Which of the following factors is most critical to ensure the model provides accurate and reliable recommendations?

Options :

A : User Interface Design

B : Compute Power

C : Data Volume

D : Model Interpretability

Answer: D

Question 4

You are comparing two generative AI models for text generation. Model A has a lower training loss but higher validation loss compared to Model B, which has higher training loss but lower validation loss. Which model should you deploy for generating content, and why?

Options :

A : Model A, because lower validation loss indicates better performance on unseen data

B : Neither, because both models need further tuning before deployment

C : Model A, because lower training loss indicates it has learned the training data better

D : Model B, because lower validation loss indicates better generalization to unseen data

Answer: D

Question 5

When dealing with a large dataset that exceeds the memory capacity of a single GPU, which approach is most effective for reading and processing the data using cuDF and Dask cuDF?

Options :

A : Load the entire dataset into cuDF on a single GPU and process it sequentially

B : Transfer data from CPU memory to multiple GPUs without partitioning the dataset

C : Utilize cuDF to compress the data before loading it into GPU memory

D : Use Dask cuDF to partition the dataset across multiple GPUs, enabling parallel processing

Answer: D

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