Question 1

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 2

Assuming we have a table called VT2 that contains vectors for mapping various shapes, colors, and sizes, what is the primary purpose of the following UPDATE statements? SQL> UPDATE vt2SET shape = 'TriangleWHERE id IN (2, 4, 7, 22, 31, 41, 44, 55);SQL> UPDATE vt2SET size = 'Medium'WHERE id IN (5, 22, 25, 32, 34, 42, 43, 53, 54, 55);

Options :

A : To add new rows of data to the VT2 table with different sizes and shapes.

B : To assign attributes like size and shape to existing vectors based on their IDs.

C : To delete rows from the VT2 table that do not meet specific criteria.

D : To modify the vector values stored in the VT2 table.

Answer: B

Question 3

Which operation is NOT permitted on tables containing VECTOR columns?

Options :

A : SELECT

B : UPDATE

C : DELETE

D : JOIN ON VECTOR columns

Answer: D

Question 4

Multi-Vector Similarity Search is typically used for:

Options :

A : Exact vector matching

B : Single-vector comparisons

C : Multi-document search

D : Image recognition

Answer: C

Question 5

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

Options :

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

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