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

Oracle 1Z0-1127-25 Online Questions & Answers

• Question 31:

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

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

• Question 32:

  Which is a characteristic of T-Few fine-tuning for Large Language Models (LLMs)?

  A. It updates all the weights of the model uniformly.
  B. It does not update any weights but restructures the model architecture.
  C. It selectively updates only a fraction of the model's weights.
  D. It increases the training time as compared to Vanilla fine-tuning.
  C. It selectively updates only a fraction of the model's weights.

• Question 33:

  What is the purpose of Retrievers in LangChain?

  A. To train Large Language Models
  B. To retrieve relevant information from knowledge bases
  C. To break down complex tasks into smaller steps
  D. To combine multiple components into a single pipeline
  B. To retrieve relevant information from knowledge bases

• Question 34:

  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 a few thousand samples or less
  D. For datasets with hundreds of thousands to millions of samples
  C. For datasets with a few thousand samples or less

• Question 35:

  Why is it challenging to apply diffusion models to text generation?

  A. Because text generation does not require complex models
  B. Because text is not categorical
  C. Because text representation is categorical unlike images
  D. Because diffusion models can only produce images
  C. Because text representation is categorical unlike images

• Question 36:

  You create a fine-tuning dedicated AI cluster to customize a foundational model with your custom training data. How many unit hours are required for fine-tuning if the cluster is active for 10 days?

  A. 480 unit hours
  B. 240 unit hours
  C. 744 unit hours
  D. 20 unit hours
  B. 240 unit hours

• Question 37:

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

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

• Question 38:

  What is the main advantage of using few-shot model prompting to customize a Large Language Model (LLM)?

  A. It allows the LLM to access a larger dataset.
  B. It eliminates the need for any training or computational resources.
  C. It provides examples in the prompt to guide the LLM to better performance with no training cost.
  D. It significantly reduces the latency for each model request.
  C. It provides examples in the prompt to guide the LLM to better performance with no training cost.

• Question 39:

  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.
  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.

• Question 40:

  Given the following code block:

  history = StreamlitChatMessageHistory(key="chat_messages")

  memory = ConversationBufferMemory(chat_memory=history)

  Which statement is NOT true about StreamlitChatMessageHistory?

  A. StreamlitChatMessageHistory will store messages in Streamlit session state at the specified key.
  B. A given StreamlitChatMessageHistory will NOT be persisted.
  C. A given StreamlitChatMessageHistory will not be shared across user sessions.
  D. StreamlitChatMessageHistory can be used in any type of LLM application.
  D. StreamlitChatMessageHistory can be used in any type of LLM application.