Databricks Generative AI Engineer Associate Question 227
Select 4You are building a semantic search application for a document repository using Databricks. You need to create and query a Vector Search index to enable efficient similarity searches over document embeddings stored in a Delta table. Which of the following steps are necessary to achieve this?
- A
Generate embeddings for the documents and store them in a Delta table.
- B
Use the
CREATE INDEXSQL statement to create a Vector Search index on the Delta table. - C
Specify the column containing the embeddings in the index creation process.
- D
Use a K-Nearest Neighbors (KNN) query to retrieve similar documents based on embeddings.
- E
Export the Delta table data to an external database before creating the index.
Show answer and explanation
Correct answers: A, B, C, D
Explanation
To enable Vector Search in Databricks, you must first generate embeddings for the data and store them in a Delta table. Next, you create a Vector Search index using the CREATE INDEX SQL statement, specifying the column containing the embeddings. Once the index is created, you can use similarity-based queries like K-Nearest Neighbors (KNN) to retrieve relevant results. Exporting the data to an external database is not required because Databricks natively supports this functionality.
- A. Correct.
Correct: Generating embeddings and storing them in a Delta table is an essential first step for enabling vector search.
- B. Correct.
Correct: The
CREATE INDEXstatement is used to create a Vector Search index on the Delta table, which allows efficient similarity searches. - C. Correct.
Correct: During the index creation process, you must specify the column that contains the embeddings to be indexed.
- D. Correct.
Correct: A K-Nearest Neighbors (KNN) query is commonly used to retrieve similar documents from the indexed embeddings.
- E. Incorrect.
Incorrect: Exporting the Delta table data to an external database is unnecessary because Databricks supports Vector Search natively on Delta tables.