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?

Options :

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

What is the purpose of the Vector Pool in Oracle Database 23ai?

Options :

A : To manage database partitioning

B : To store HNSW vector indexes and IVF index metadata

C : To enable longer SQL execution

D : To store non-vector data types

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?

Options :

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

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

Options :

A : VECTOR_DIMENSION_FORMAT

B : VECTOR_CHUNKS

C : VECTOR_NORM

D : VECTOR_EMBEDDING

Answer: A

Question 5

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

Options :

A : Insertion only

B : Insertion, updates, deletion, and loading

C : Insertion and updates

D : Insertion, updates, and deletion

Answer: B

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