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

Oracle 1Z0-1127-25 Online Questions & Answers

• Question 51:

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

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

• Question 52:

  Which is NOT a built-in memory type in LangChain?

  A. ConversationImageMemory
  B. ConversationBufferMemory
  C. ConversationSummaryMemory
  D. ConversationTokenBufferMemory
  A. ConversationImageMemory

• Question 53:

  An LLM emits intermediate reasoning steps as part of its responses. Which of the following techniques is being utilized?

  A. In-context Learning
  B. Step-Back Prompting
  C. Least-to-Most Prompting
  D. Chain-of-Thought
  D. Chain-of-Thought

• Question 54:

  What is the role of temperature in the decoding process of a Large Language Model (LLM)?

  A. To increase the accuracy of the most likely word in the vocabulary
  B. To determine the number of words to generate in a single decoding step
  C. To decide to which part of speech the next word should belong
  D. To adjust the sharpness of probability distribution over vocabulary when selecting the next word
  D. To adjust the sharpness of probability distribution over vocabulary when selecting the next word

• Question 55:

  What does the Ranker do in a text generation system?

  A. It generates the final text based on the user's query.
  B. It sources information from databases to use in text generation.
  C. It evaluates and prioritizes the information retrieved by the Retriever.
  D. It interacts with the user to understand the query better.
  C. It evaluates and prioritizes the information retrieved by the Retriever.

• Question 56:

  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.
  C. Increasing temperature flattens the distribution, allowing for more varied word choices.

• Question 57:

  What is LangChain?

  A. A JavaScript library for natural language processing
  B. A Python library for building applications with Large Language Models
  C. A Java library for text summarization
  D. A Ruby library for text generation
  B. A Python library for building applications with Large Language Models

• Question 58:

  Which component of Retrieval-Augmented Generation (RAG) evaluates and prioritizes the information retrieved by the retrieval system?

  A. Retriever
  B. Encoder-Decoder
  C. Generator
  D. Ranker
  D. Ranker

• Question 59:

  What is the primary function of the "temperature" parameter in the OCI Generative AI Generation models?

  A. Controls the randomness of the model's output, affecting its creativity
  B. Specifies a string that tells the model to stop generating more content
  C. Assigns a penalty to tokens that have already appeared in the preceding text
  D. Determines the maximum number of tokens the model can generate per response
  A. Controls the randomness of the model's output, affecting its creativity

• Question 60:

  What does in-context learning in Large Language Models involve?

  A. Pretraining the model on a specific domain
  B. Training the model using reinforcement learning
  C. Conditioning the model with task-specific instructions or demonstrations
  D. Adding more layers to the model
  C. Conditioning the model with task-specific instructions or demonstrations