Question 1

After fine-tuning a model in a dedicated AI cluster, which option allows you to deploy the model for real-world use cases?

Options :

A : Directly exporting the model from the cluster for local deployment.

B : Configuring the cluster for continuous inference on incoming requests.

C : Saving the fine-tuned model as a file and uploading it to a separate service.

D : Creating an endpoint within the dedicated AI cluster for model access.

Answer: D

Question 2

Which of the following is NOT a common application of Large Language Models (LLMs)?

Options :

A : Chatbots and virtual assistants

B : Machine translation of languages

C : Anomaly detection in network traffic

D : Content creation and text summarization

Answer: C

Question 3

In the context of fine-tuning with OCI Generative AI Service, what is the role of a custom dataset?

Options :

A : It defines the new tasks the model should be able to perform.

B : It provides additional training data specific to your domain.

C : It acts as a validation set to evaluate the fine-tuned model-s performance.

D : It replaces the pre-trained weights of the base model entirely.

Answer: B

Question 4

What potential drawback can arise when using overly complex or lengthy prompts for LLMs?

Options :

A : The LLM might generate a more creative and interesting output.

B : The LLM might struggle to focus on the essential aspects of the task.

C : There is no downside; longer prompts always provide better results.

Answer: B

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