Question 1

Given the following prompts used with a Large Language Model, classify each as employing the Chain-of- Thought, Least-to-most, or Step-Back prompting technique.1. Calculate the total number of wheels needed for 3 cars. Cars have 4 wheels each. Then, use the total number of wheels to determine how many sets of wheels we can buy with $200 if one set (4 wheels) costs $50.2. Solve a complex math problem by first identifying the formula needed, and then solve a simpler version of the problem before tackling the full question.3. To understand the impact of greenhouse gases on climate change, let’s start by defining what greenhouse gases are. Next, we’ll explore how they trap heat in the Earth’s atmosphere.

Options :

A : 1: Chain-of-Thought, 2: Step-Back, 3: Least-to-most

B : 1: Step-Back, 2: Chain-of-Thought, 3: Least-to-most

C : 1: Chain-of-Thought, 2: Least-to-most, 3: Step-Back

D : 1: Least-to-most, 2: Chain-of-Thought, 3: Step-Back

Answer: C

Question 2

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 3

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 4

What issue might arise from using small data sets with the Vanilla fine-tuning method in the OCI Generative AI service?

Options :

A : Model Drift

B : Data Leakage

C : Underfitting

D : Overfitting

Answer: C

Question 5

Which statement best describes the role of encoder and decoder models in natural language processing?

Options :

A : Encoder models and decoder models both convert sequences of words into vector representations without generating new text.

B : Encoder models are used only for numerical calculations, whereas decoder models are used to interpret the calculated numerical values back into text.

C : Encoder models convert a sequence of words into a vector representation, and decoder models take this vector representation to generate a sequence of words.

D : Encoder models take a sequence of words and predict the next word in the sequence, whereas decoder models convert a sequence of words into a numerical representation.

Answer: C