Oracle 1Z0-184-25 Online Practice Questions and Exam Preparation

Oracle 1Z0-184-25 Online Questions & Answers

• Question 51:

  Which operation is NOT permitted on tables containing VECTOR columns?

  A. SELECT
  B. UPDATE
  C. DELETE
  D. JOIN ON VECTOR columns
  D. JOIN ON VECTOR columns

• Question 52:

  Which Oracle Cloud Infrastructure (OCI) service is directly integrated with Select AI?

  A. OCI Language
  B. OCI Generative AI
  C. OCI Vision
  D. OCI Data Science
  B. OCI Generative AI

• Question 53:

  What is the significance of splitting text into chunks in the process of loading data into Oracle AI Vector Search?

  A. To reduce the computational burden on the embedding model
  B. To facilitate parallel processing of the data during vectorization
  C. To minimize token truncation as each vector embedding model has its own maximum token limit
  C. To minimize token truncation as each vector embedding model has its own maximum token limit

• Question 54:

  Which function should you use to determine the storage format of a vector?

  A. VECTOR_DIMENSION_FORMAT
  B. VECTOR_CHUNKS
  C. VECTOR_NORM
  D. VECTOR_EMBEDDING
  A. VECTOR_DIMENSION_FORMAT

• Question 55:

  What is the correct order of steps for building a RAG application using PL/SQL in Oracle Database 23ai?

  A. Load ONNX Model, Vectorize Question, Load Document, Split Text into Chunks, Create Embeddings, Perform Vector Search, Generate Output
  B. Load Document, Split Text into Chunks, Load ONNX Model, Create Embeddings, Vectorize Question, Perform Vector Search, Generate Output
  C. Vectorize Question, Load ONNX Model, Load Document, Split Text into Chunks, Create Embeddings, Perform Vector Search, Generate Output
  D. Load Document, Load ONNX Model, Split Text into Chunks, Create Embeddings, VectorizeQuestion, Perform Vector Search, Generate Output
  B. Load Document, Split Text into Chunks, Load ONNX Model, Create Embeddings, Vectorize Question, Perform Vector Search, Generate Output

• Question 56:

  When generating vector embeddings outside the database, what is the most suitable option for storing the embeddings for later use?

  A. In a CSV file
  B. In a binary FVEC file with the relational data in a CSV file
  C. In the database as BLOB (Binary Large Object) data
  D. In a dedicated vector database
  D. In a dedicated vector database

• Question 57:

  Which statement best describes the core functionality and benefit of Retrieval Augmented Generation (RAG) in Oracle Database 23ai?

  A. It empowers LLMs to interact with private enterprise data stored within the database, leading to more context-aware and precise responses to user queries
  B. It primarily aims to optimize the performance and efficiency of LLMs by using advanced data retrieval techniques, thus minimizing response times and reducing computational overhead
  C. It allows users to train their own specialized LLMs directly within the Oracle Database environment using their internal data, thereby reducing reliance on external AI providers
  D. It enables Large Language Models (LLMs) to access and process real-time data streams from diverse sources to generate the most up-to-date insights
  A. It empowers LLMs to interact with private enterprise data stored within the database, leading to more context-aware and precise responses to user queries

• Question 58:

  You are asked to fetch the top five vectors nearest to a query vector, but only for a specific category of documents. Which query structure should you use?

  A. Use UNION ALL with vector operations
  B. Perform the similarity search without a WHERE clause
  C. Apply relational filters and a similarity search in the query
  D. Use VECTOR_INDEX_HINT and NO WHERE clause
  C. Apply relational filters and a similarity search in the query

• Question 59:

  In the following Python code, what is the significance of prepending the source filename to each text chunk before storing it in the vector database?

  bash

  CollapseWrapCopy

  docs = [{"text": filename + "|" + section, "path": filename} for filename, sections in faqs.items() for section in sections]

  # Sample the resulting data docs[:2]

  A. It preserves context and aids in the retrieval process by associating each vectorized chunk with its original source file
  B. It helps differentiate between chunks from different files but has no impact on vectorization
  C. It speeds up the vectorization process by providing a unique identifier for each chunk
  D. It improves the accuracy of the LLM by providing additional training data
  A. It preserves context and aids in the retrieval process by associating each vectorized chunk with its original source file

• Question 60:

  What happens when you attempt to insert a vector with an incorrect number of dimensions into a VECTOR column with a defined number of dimensions?

  A. The database truncates the vector to fit the defined dimensions
  B. The database pads the vector with zeros to match the defined dimensions
  C. The database ignores the defined dimensions and inserts the vector as is
  D. The insert operation fails, and an error message is thrown
  D. The insert operation fails, and an error message is thrown