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Question No 1

When generating vector embeddings outside the database, what is the most suitable option for storing the embeddings for later use?

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Question No 2

When generating vector embeddings for a new dataset outside of Oracle Database 23ai, which factor is crucial to ensure meaningful similarity search results?

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The choice of programming language used to process the dataset (e.g., Python, Java)

The physical location where the vector embeddings are stored

The storage format of the new dataset (e.g., CSV, JSON)

The same vector embedding model must be used for vectorizing the data and creating a query vector

Question No 3

You are working with vector search in Oracle Database 23ai and need to ensure the integrity of your vector data during storage and retrieval. Which factor is crucial for maintaining the accuracy and reliability of your vector search results?

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Using the same embedding model for both vector creation and similarity search

Regularly updating vector embeddings to reflect changes in the source data

The specific distance algorithm employed for vector comparisons

The physical storage location of the vector data

Question No 4

Which DDL operation is NOT permitted on a table containing a VECTOR column in Oracle Database 23ai?

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Creating a new table using CTAS (CREATE TABLE AS SELECT) that includes the VECTOR column from the original table

Dropping an existing VECTOR column from the table

Modifying the data type of an existing VECTOR column to a non - VECTOR type

Adding a new VECTOR column to the table

Question No 5

Which SQL statement correctly adds a VECTOR column named "v" with 4 dimensions and FLOAT32 format to an existing table named "my_table"?

Choose the Choices:

ALTER TABLE my_table MODIFY (v VECTOR(4, FLOAT32))

ALTER TABLE my_table ADD (v VECTOR(4, FLOAT32))

UPDATE my_table SET v = VECTOR(4, FLOAT32)

ALTER TABLE my_table ADD v VECTOR(4, FLOAT32)

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