Question 1

What is Retrieval Augmented Generation (RAG)?

Options :

A : RAG is an architecture used to optimize the output of an LLM by retraining the model with domain-specific data.

B : RAG is a methodology that combines an information retrieval component with a response generator.

C : RAG is a method for manipulating and generating text-based data using Transformer-based LLMs.

D : RAG is a technique used to fine-tune pre-trained LLMs for improved performance.

Answer: B

Question 2

When performing GPU-accelerated data manipulation, which of the following operations is most likely to benefit from GPU acceleration?

Options :

A : Executing simple arithmetic operations on a small dataset

B : Sorting a small dataset of a few hundred rows

C : Applying complex transformations to large-scale image datasets

D : Loading data from a slow disk drive to memory

Answer: C

Question 3

You are working on a customer service chatbot that needs to provide personalized responses based on user history and preferences. The chatbot must integrate with a customer relationship management (CRM) system to retrieve relevant user data. Your task is to determine the most efficient way to enhance the chatbot's ability to deliver personalized responses. Which approach would most effectively enable the chatbot to deliver personalized responses?

Options :

A : Integrate the chatbot with the CRM system using API calls to fetch user data in real-time.

B : Use a rule-based system to generate responses based on predefined scenarios.

C : Train the chatbot on a large, generic dataset without integrating CRM data.

D : Store user preferences in a local database and periodically sync with the CRM.

Answer: A

Question 4

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

Question 5

You are curating a dataset for a retrieval-augmented generation (RAG) system that will assist financial analysts by generating reports based on a vast collection of financial documents. However, you notice that many documents contain industry-specific jargon that might be difficult for the LLM to process effectively. What is the best approach to ensure the model accurately handles this specialized vocabulary?

Options :

A : Preprocess the dataset to replace jargon with simpler terms

B : Train a domain-specific word embedding model to capture the jargon’s meaning

C : Manually filter out documents with too much jargon

D : Fine-tune the LLM on a general financial news dataset

Answer: B

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