Databricks Generative AI Engineer Associate Question 228
Select 4You are tasked with building a vector search index for a product recommendation system using Databricks. After creating the vector embeddings from product descriptions, you need to store them in a vector search index and perform queries to find similar products. Which steps are required to successfully create and query the vector search index in Databricks?
- A
Generate vector embeddings for the data and store them in a Delta table.
- B
Create a vector search index using the
CREATE INDEXstatement on the Delta table with vector embeddings. - C
Query the vector search index using cosine similarity or Euclidean distance-based functions.
- D
Perform a full-text search query to retrieve the most relevant results from the vector search index.
- E
Ensure the vector embeddings are normalized before creating the vector search index for accurate similarity comparisons.
- F
Use the Databricks MLflow tracking server to directly create and query vector search indices.
Show answer and explanation
Correct answers: A, B, C, E
Explanation
To successfully create and query a vector search index in Databricks, you need to generate vector embeddings, store them in a Delta table, create the index using SQL commands like CREATE INDEX, and query it using similarity metrics such as cosine similarity or Euclidean distance. Normalizing the embeddings is also crucial to ensure accurate similarity results. Full-text search and MLflow tracking server are unrelated to vector search operations.
- A. Correct.
Correct: Generating vector embeddings and storing them in a Delta table is a necessary first step to create a vector search index in Databricks.
- B. Correct.
Correct: The
CREATE INDEXstatement is used to create a vector search index on a Delta table containing vector embeddings. - C. Correct.
Correct: Querying the vector search index involves using similarity metrics such as cosine similarity or Euclidean distance to retrieve similar items.
- D. Incorrect.
Incorrect: Full-text search is unrelated to vector search. Vector search focuses on similarity matching using embeddings, not text-based relevance.
- E. Correct.
Correct: Normalizing vector embeddings ensures more accurate similarity computations, which is critical for indexing and querying.
- F. Incorrect.
Incorrect: MLflow tracking server is used for experiment tracking and model management, not for creating or querying vector search indices.