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Question 1

When utilizing pre-trained models for text embedding in OCI Generative AI, what is the resulting output?

Options :

A : A human-readable summary of the key topics within the text.

B : A sentiment score indicating the overall positive or negative tone of the text.

C : A numerical representation capturing the semantic meaning of the text.

D : A classification label assigning the text to a predefined category.

Answer: C

Question 2

Which component of Retrieval-Augmented Generation (RAG) evaluates and prioritizes the information retrieved by the retrieval system?

Options :

A : Encoder-decoder

B : Ranker

C : Generator

D : Retriever

Answer: B

Question 3

When designing a LangChain for an LLM application with RAG, what element should ensure alignment between retrieved documents and LangChain prompts?

Options :

A : The programming language used to interact with the OCI Generative AI service.

B : The size and complexity of the LangChain model architecture.

C : The selection of appropriate keywords in both prompts and retrieved documents

D : The choice of a specific distance metric for the vector database search.

Answer: C

Question 4

How are fine-tuned customer models stored to enable strong data privacy and security in the OCI Generative AI service?

Options :

A : Shared among multiple customers for efficiency

B : Stored in Object Storage encrypted by default

C : Stored in Key Management service

D : Stored in an unencrypted form in Object Storage

Answer: B

Question 5

Which is a key advantage of using T-Few over Vanilla fine-tuning in the OCI Generative AI service?

Options :

A : Increased model interpretability

B : Enhanced generalization to unseen data

C : Faster training time and lower cost

D : Reduced model complexity

Answer: C