Question 1

When fine-tuning an LLM for a specific application, why is it essential to perform exploratory data analysis (EDA) on the new training dataset?

Options :

A : To uncover patterns and anomalies in the dataset

B : To select the appropriate learning rate for the model

C : To assess the computing resources required for fine-tuning

D : To determine the optimum number of layers in the neural network

Answer: A

Question 2

You have deployed a Large Language Model (LLM) for a customer support chatbot. The chatbot performs well in most scenarios but struggles with ambiguous queries, often giving incomplete or irrelevant responses. Which of the following approaches is most likely to improve the chatbot's performance in handling ambiguous queries?

Options :

A : Implement a fallback mechanism to ask clarifying questions when the query is ambiguous.

B : Reduce the number of layers in the LLM to simplify its output.

C : Increase the temperature setting in the model’s inference process.

D : Increase the size of the training dataset with more diverse examples.

Answer: A

Question 3

You are developing a generative AI model that needs to generate high-quality images from textual descriptions in real-time. Which two of the following approaches will best optimize the performance and quality of your model given the hardware constraints of a GPU with limited memory? (Select two)

Options :

A : Implement mixed precision training

B : Increase the batch size to the maximum the GPU can handle

C : Implement data parallelism across multiple GPUs

D : Use gradient checkpointing

E : Use a larger learning rate to converge faster

Answer: A,D

Question 4

You are developing a chatbot using a generative AI model to assist users with technical troubleshooting. The responses generated by the model are often too verbose, overwhelming the user with unnecessary details. What prompt engineering strategy would you use to make the responses more concise and user-friendly?

Options :

A : Use multiple prompts to break down the response into smaller sections

B : Explicitly instruct the model to generate concise and to-the-point responses in the prompt

C : Set the model to a higher temperature to reduce verbosity

D : Lengthen the prompt to include more background information

Answer: B

Question 5

You are preparing a large-scale dataset for training a language model aimed at detecting hate speech in social media posts. The dataset is diverse, containing text in multiple languages and various social media platforms. You need to ensure high-quality labels and annotations for effective training. Which of the following approaches would most effectively ensure the quality and relevance of the dataset annotations?

Options :

A : Use automated keyword detection tools to label the dataset and skip manual review

B : Crowdsource the annotation task to non-experts for faster labeling

C : Label the dataset using a simple binary system (hate speech or not) without considering context or severity

D : Engage domain experts to manually review and annotate a portion of the dataset, and use these as a gold standard to train an automated labeling tool

Answer: D

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