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

Which is a distinguishing feature of "Parameter-Efficient Fine-Tuning (PEFT)" as opposed to classic "Fine-tuning" in Large Language Model training?

• A.  PEFT modifies all parameters and is typically used when no training data exists.
• B.  PEFT involves only a few or new parameters and uses labeled, task-specific data.
• C.  PEFT does not modify any parameters but uses soft prompting with unlabeled data.
• D.  PEFT modifies all parameters and uses unlabeled, task-agnostic data.

What does a cosine distance of 0 indicate about the relationship between two embeddings?

• A.  They have the same magnitude
• B.  They are unrelated
• C.  They are completely dissimilar
• D.  They are similar in direction

In which scenario is soft prompting especially appropriate compared to other training styles?

• A.  When the model needs to be adapted to perform well in a different domain it was not originally trained on.
• B.  When there is a need to add learnable parameters to a Large Language Model (LLM) without task-specific training.
• C.  When there is a significant amount of labeled, task-specific data available.
• D.  When the model requires continued pre-training on unlabeled data.

When should you use the T-Few fine-tuning method for training a model?

• A.  For complicated semantic understanding improvement
• B.  For models that require their own hosting dedicated AI cluster
• C.  For datasets with hundreds of thousands to millions of samples
• D.  For datasets with a few thousand samples or less

What does the RAG Sequence model do in the context of generating a response?

• A.  It retrieves relevant documents only for the initial part of the query and ignores the rest.
• B.  It retrieves a single relevant document for the entire input query and generates a response based on that alone.
• C.  It modifies the input query before retrieving relevant documents to ensure a diverse response.
• D.  For each input query, it retrieves a set of relevant documents and considers them together to generate a cohesive response.