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

Compared to traditional LLM generation, how does RAG potentially impact the training data requirements for OCI Generative AI projects?

Options :

A : RAG always requires a significantly larger dataset for training the LLM.

B : RAG can potentially lead to effective results even with smaller training datasets.

C : RAG eliminates the need for training data altogether, relying solely on retrieval.

D : The impact of RAG on training data requirements depends solely on th

Answer: B

Question 3

When fine-tuning a pre-trained model in OCI Generative AI, what does the T-Few technique offer?

Options :

A : Reduced training time compared to traditional fine-tuning methods.

B : Improved accuracy on tasks requiring large amounts of training data.

C : The ability to fine-tune models with a wider range of data formats.

D : Increased interpretability of the fine-tuned model-s decision-making

Answer: A

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

Which statement describes the difference between "Top k" and "Top p" in selecting the next token in the OCI Generative AI Generation models?

Options :

A : "Top k" and "Top p" both select from the same set of tokens but use different methods to prioritize them based on frequency.

B : "Top k" and "Top p" are identical in their approach to token selection but differ in their application of penalties to tokens.

C : "Top k" selects the next token based on its position in the list of probable tokens, whereas "Top p" selects based on the cumulative probability of the top tokens.

D : "Top k" considers the sum of probabilities of the top tokens, whereas "Top p" selects from the "Top k" tokens sorted by probability.

Answer: C

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