Oracle 1Z0-1127-25 Online Practice Questions and Exam Preparation

Oracle 1Z0-1127-25 Online Questions & Answers

• Question 81:

  Which role does a "model endpoint" serve in the inference workflow of the OCI Generative AI service?

  A. Updates the weights of the base model during the fine-tuning process
  B. Serves as a designated point for user requests and model responses
  C. Evaluates the performance metrics of the custom models
  D. Hosts the training data for fine-tuning custom models
  B. Serves as a designated point for user requests and model responses

• Question 82:

  How are prompt templates typically designed for language models?

  A. As complex algorithms that require manual compilation
  B. As predefined recipes that guide the generation of language model prompts
  C. To be used without any modification or customization
  D. To work only with numerical data instead of textual content
  B. As predefined recipes that guide the generation of language model prompts

• Question 83:

  Which is a key characteristic of Large Language Models (LLMs) without Retrieval Augmented Generation (RAG)?

  A. They always use an external database for generating responses.
  B. They rely on internal knowledge learned during pretraining on a large text corpus.
  C. They cannot generate responses without fine-tuning.
  D. They use vector databases exclusively to produce answers.
  B. They rely on internal knowledge learned during pretraining on a large text corpus.

• Question 84:

  In the context of generating text with a Large Language Model (LLM), what does the process of greedy decoding entail?

  A. Selecting a random word from the entire vocabulary at each step
  B. Picking a word based on its position in a sentence structure
  C. Choosing the word with the highest probability at each step of decoding
  D. Using a weighted random selection based on a modulated distribution
  C. Choosing the word with the highest probability at each step of decoding

• Question 85:

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

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

• Question 86:

  When does a chain typically interact with memory in a run within the LangChain framework?

  A. Only after the output has been generated
  B. Before user input and after chain execution
  C. After user input but before chain execution, and again after core logic but before output
  D. Continuously throughout the entire chain execution process
  C. After user input but before chain execution, and again after core logic but before output

• Question 87:

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

  A. PEFT involves only a few or new parameters and uses labeled, task-specific data.
  B. PEFT modifies all parameters and is typically used when no training data exists.
  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.
  A. PEFT involves only a few or new parameters and uses labeled, task-specific data.

• Question 88:

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

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