Question 1

How is "Prompt Engineering" different from "Fine-tuning" in the context of Large Language Models

Options :

A : Both involve retraining the model, but Prompt Engineering does it more often.

B : Prompt Engineering creates input prompts, while Fine-tuning retrains the model on specific data.

C : Prompt Engineering modifies training data, while Fine-tuning alters the model's structure.

D : Prompt Engineering adjusts the model's parameters, while Fine-tuning crafts input prompts.

Answer: B

Question 2

What is "in-context learning" in the realm of Large Language Models (LLMs)?

Options :

A : Teaching a model through zero-shot learning

B : Modifying the behavior ofa pretrained LLM permanently

C : Providing a few examples ofa target task via the input prompt

D : Training a model on a diverse range of tasks

Answer: C

Question 3

Which feature is NOT available as part of OCI Speech capabilities?

Options :

A : Provides timestamped, grammatically accurate transcriptions

B : Transcribes audio and video files into text

C : Uses extensive data science experience to operate

D : Supports multiple languages including English, Spanish, and Portuguese

Answer: C

Question 4

What is the key feature of Recurrent Neural Networks (RNNs)?

Options :

A : They process data in parallel.

B : They are primarily used for image recognition tasks.

C : They do not have an internal state.

D : They have a feedback loop that allows information to persist across different time steps.

Answer: D