Free Oracle 1Z0-184-25 Exam Questions

Become Oracle Certified with updated 1Z0-184-25 exam questions and correct answers

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Total 108 Questions | Updated On: Dec 16, 2025

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Question 1

How does an application use vector similarity search to retrieve relevant information from a database, and how is this information then integrated into the generation process?

A : Encodes the question and database chunks into vectors, finds the most similar using cosine similarity, and includes them in the LLM prompt

B : Trains a separate LLM on the database and uses it to answer, ignoring the general LLM

C : Converts the question to keywords, searches for matches, and inserts the text into the response

D : Clusters similar text chunks and randomly selects one from the most relevant cluster

Answer: A

Question 2

How can vector data be manipulated within an Oracle Database using Data Manipulation Language (DML)?

A : Insertion only

B : Insertion, updates, deletion, and loading

C : Insertion and updates

D : Insertion, updates, and deletion

Answer: B

Question 3

You are tasked with creating a table to store vector embeddings with the following characteristics: Each vector must have exactly 512 dimensions, and the dimensions should be stored as 32-bitfloating point numbers. Which SQL statement should you use?

A : CREATE TABLE vectors (id NUMBER, embedding VECTOR(512))

B : CREATE TABLE vectors (id NUMBER, embedding VECTOR)

C : CREATE TABLE vectors (id NUMBER, embedding VECTOR(*, INT8

D : CREATE TABLE vectors (id NUMBER, embedding VECTOR(512, FLOAT32))

Answer: D

Question 4

What is the primary purpose of the VECTOR NORM() function in Oracle Database 23ai?

A : To return the magnitude or length of a vector.

B : To convert a vector to a string representation.

C : To determine the number of dimensions in a vector.

D : To calculate the Euclidean distance between two vectors.

Answer: A

Question 5

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

Answer: B

Page: 1 / 22

Total 108 Questions | Updated On: Dec 16, 2025

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