Question 1

Why is it challenging to apply diffusion models to text generation?

Options :

A : Because text generation does not require complex models

B : Because text is not categorical

C : Because text representation is categorical unlike images

Answer: C

Question 2

For tasks requiring text summarization with OCI Generative AI, which of the following pre-trained model capabilities is most relevant?

Options :

A : High accuracy in sentiment analysis for identifying positive/negative emotions.

B : Ability to translate text from one language to another with fluency.

C : Skill in extracting key points and condensing factual information from text.

D : Capacity to generate different creative text formats like poems or

Answer: C

Question 3

What does accuracy measure in the context of fine-tuning results for a generative model?

Options :

A : The depth of the neural network layers used in the model

B : The number of predictions a model makes, regardless of whether they are correct or incorrect

C : How many predictions the model made correctly out of all the predictions in an evaluation

D : The proportion of incorrect predictions made by the model during an evaluation

Answer: C

Question 4

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 5

How are fine-tuned customer models stored to enable strong data privacy and security in the OCI Generative AI service?

Options :

A : Shared among multiple customers for efficiency

B : Stored in Object Storage encrypted by default

C : Stored in Key Management service

D : Stored in an unencrypted form in Object Storage

Answer: B

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