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

When is fine-tuning an appropriate method for customizing a Large Language Model (LLM)?

• A.  When you want to optimize the model without any instructions
• B.  When the LLM requires access to the latest data for generating outputs
• C.  When the LLM already understands the topics necessary for text generation
• D.  When the LLM does not perform well on a task and the data for prompt engineering is too large

What is the function of the Generator in a text generation system?

• A.  To generate human-like text using the information retrieved and ranked, along with the user's original query
• B.  To collect user queries and convert them into database search terms
• C.  To rank the information based on its relevance to the user's query
• D.  To store the generated responses for future use

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

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

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

• A.  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.
• 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.  Both Fine-tuning and PEFT require the model to be trained from scratch on new data, making them equally data and computationally intensive.
• D.  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.

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:

• A.  "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."
• B.  "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."
• C.  "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."A. 1: Step-Back, 2: Chain-of-Thought, 3: Least-to-MostB. 1: Least-to-Most, 2: Chain-of-Thought, 3: Step-BackC. 1: Chain-of-Thought, 2: Step-Back, 3: Least-to-MostD. 1: Chain-of-Thought, 2: Least-to-Most, 3: Step-Back