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

What type of data, can be used for creating Vector Embeddings?

Options :

A : Image data

B : Audio data

C : Text data

D : All of the above

Answer: D

Question 3

A machine learning team is using IVF indexes in Oracle Database 23ai to find similar images in a large dataset. During testing, they observe that the search results are often incomplete, missing relevant images. They suspect the issue lies in the number of partitions probed. How should they improve the search accuracy?

Options :

A : Add the TARGET_ACCURACY clause to the query with a higher value for the accuracy

B : Change the index type to HNSW for better accuracy

C :

Increase the VECTOR_MEMORY_SIZE initialization parameter

D : Re-create the index with a higher EFCONSTRUCTION value

Answer: A

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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