GENERATIVE-AI-ENGINEER-ASSOCIATE · Question #88
A Generative AI Engineer using the code below to test setting up a vector store: Assuming they intend to use Databricks managed embeddings with the default embedding model, what should be the next…
The correct answer is B. vsc.create_delta_sync_index(). After creating a VectorSearchClient, the next step when using Databricks managed embeddings is to create a Delta Sync index, which automatically handles embedding generation using the default model.
Question
A Generative AI Engineer using the code below to test setting up a vector store:
Assuming they intend to use Databricks managed embeddings with the default embedding model, what should be the next logical function call?
Exhibit
Options
- Avsc.get_index()
- Bvsc.create_delta_sync_index()
- Cvsc.create_direct_access_index()
- Dvsc.similarity_search()
How the community answered
(41 responses)- A2% (1)
- B88% (36)
- C2% (1)
- D7% (3)
Why each option
After creating a VectorSearchClient, the next step when using Databricks managed embeddings is to create a Delta Sync index, which automatically handles embedding generation using the default model.
vsc.get_index() retrieves an existing index by name and will fail if called before the index has been created.
vsc.create_delta_sync_index() creates a Vector Search index that syncs from a Delta table and uses Databricks-managed embeddings to automatically embed source data - this is the correct next call when intending to use the default managed embedding model without providing custom embeddings.
vsc.create_direct_access_index() is used when the caller will supply pre-computed embeddings directly, not when using Databricks managed embeddings.
vsc.similarity_search() queries an existing index and cannot be called before the index has been created and populated.
Concept tested: Databricks Vector Search index creation with managed embeddings
Source: https://docs.databricks.com/en/generative-ai/vector-search.html
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