Question 1

When is fine-tuning an appropriate method for customizing a Large Language Model (LLM)?

Options :

A : When the LLM requires access to the latest data for generating outputs

B : When the LLM already understands the topics necessary for text generation

C : When the LLM does not perform well on a task and the data for prompt engineering is too large

Answer: C

Question 2

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 3

What benefit does using vector databases offer in the context of building fault-tolerant LLM applications with OCI Generative AI?

Options :

A : They enable real-time generation of new vector representations for unseen data.

B : They improve the interpretability of the LLM model-s decision-making process

C : They provide a mechanism for caching frequently retrieved documents.

D : They allow for incremental updates to the vector database without retraining the LLM model.

Answer: D

Question 4

For tasks requiring text summarization with OCI Generative AI, which of the following pre-trained model capabilities is most relevant?

Options :

A : High accuracy in sentiment analysis for identifying positive/negative emotions.

B : Ability to translate text from one language to another with fluency.

C : Skill in extracting key points and condensing factual information from text.

D : Capacity to generate different creative text formats like poems or

Answer: C

Question 5

In the context of Retrieval-Augmented Generation (RAG) for LLM applications, how does a vector database contribute to the retrieval process?

Options :

A : By storing pre-computed summaries of relevant documents.

B : By enabling efficient search for similar textual representations based on semantic closeness.

C : By directly generating responses based on vector representations of input prompts.

D : By training the LLM model on the vector data itself.

Answer: B

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