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

When deploying a pre-trained model for text generation in OCI Generative AI, which pricing factor should be prioritize?

Options :

A : The cost associated with storing the pre-trained model itself.

B : The number of API calls made to the deployed model for text generation.

C : The hourly rate of the underlying AI cluster utilized during deployment.

D : The size and complexity of the user-defined prompt for generation tasks.

Answer: B

Question 3

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

Question 4

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 5

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

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