Question 1

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 2

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 3

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 4

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

Options :

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 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

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