Free Oracle 1Z0-1127-25 Exam Dumps Questions & Answers

Which is the main characteristic of greedy decoding in the context of language model word prediction?

• A.  It selects words based on a flattened distribution over the vocabulary.
• B.  It requires a large temperature setting to ensure diverse word selection.
• C.  It chooses words randomly from the set of less probable candidates.
• D.  It picks the most likely word at each step of decoding.

What is the purpose of frequency penalties in language model outputs?

• A.  To penalize tokens that have already appeared, based on the number of times they have been used
• B.  To randomly penalize some tokens to increase the diversity of the text
• C.  To reward the tokens that have never appeared in the text
• D.  To ensure that tokens that appear frequently are used more often

Which statement is true about Fine-tuning and Parameter-Efficient Fine-Tuning (PEFT)?

• A.  Fine-tuning and PEFT do not involve model modification; they differ only in the type of data used for training, with Fine-tuning requiring labeled data and PEFT using unlabeled data.
• B.  PEFT requires replacing the entire model architecture with a new one designed specifically for the new task, making it significantly more data-intensive than Fine-tuning.
• C.  Fine-tuning requires training the entire model on new data, often leading to substantial computational costs, whereas PEFT involves updating only a small subset of parameters, minimizing computational requirements and data needs.
• D.  Both Fine-tuning and PEFT require the model to be trained from scratch on new data, making them equally data and computationally intensive.

How does the temperature setting in a decoding algorithm influence the probability distribution over the vocabulary?

• A.  Increasing temperature removes the impact of the most likely word.
• B.  Decreasing temperature broadens the distribution, making less likely words more probable.
• C.  Increasing temperature flattens the distribution, allowing for more varied word choices.
• D.  Temperature has no effect on the probability distribution; it only changes the speed of decoding.

Which is NOT a category of pretrained foundational models available in the OCI Generative AI service?

• A.  Summarization models
• B.  Generation models
• C.  Embedding models
• D.  Translation models