Question 1

In the context of OCI Generative AI, what is the primary function of the Retriever component in Retrieval Augmented Generation (RAG)?

Options :

A : To generate creative text formats like poems or code snippets.

B : To identify relevant passages from a large corpus of text data.

C : To fine-tune a pre-trained model for a specific task using your data.

D : To evaluate the quality and coherence of the generated text.

Answer: B

Question 2

How can fine-tuning an LLM model with OCI Generative AI contribute to improved performance of semantic search in your application?

Options :

A : By enabling the LLM to directly generate documents based on semantic similarity.

B : By allowing the LLM to interpret the retrieved documents and provide more context-aware responses.

C : By improving the accuracy of vector representations generated for the documents in the database.

D : By optimizing the search algorithms used by the vector database for specific LLM tasks.

Answer: B

Question 3

What is the purpose of Retrieval Augmented Generation (RAG) in text generation?

Options :

A : To generate text based only on the model-s internal knowledge without external data

B : To generate text using extra information obtained from an external data source

C : To store text in an external database without using it for generation

D : To retrieve text from an external source and present it without any modifications

Answer: B

Question 4

When fine-tuning a pre-trained model in OCI Generative AI, what does the T-Few technique offer?

Options :

A : Reduced training time compared to traditional fine-tuning methods.

B : Improved accuracy on tasks requiring large amounts of training data.

C : The ability to fine-tune models with a wider range of data formats.

D : Increased interpretability of the fine-tuned model-s decision-making

Answer: A

Question 5

What does "k-shot prompting" refer to when using Large Language Models for task-specific applications?

Options :

A : Explicitly providing k examples of the intended task in the prompt to guide the model’s output

B : Limiting the model to only k possible outcomes or answers for a given task

C : Providing the exact k words in the prompt to guide the model’s response

D : The process of training the model on k different tasks simultaneously to improve its versatility

Answer: A

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