A RAG application is returning results from a Vector Search index, but the top results are not relevant to the user's query. The team suspects the query embedding is not aligned with the document embeddings. What is the most likely cause and fix?

The index endpoint is under-provisioned; scale up the endpoint to improve search quality.

The query is being embedded with a different model than the one used for the index; use the same embedding model for both.

The query is too long; truncate it to the first 50 tokens before embedding.

The index is not synced; trigger a manual full refresh to update the embeddings.

DatabricksCertified Generative AI Engineer Associate

Domain 4Objective 1

Build and Configure RAG Applications GENERATIVE-AI-ENGINEER-ASSOCIATE Practice Questions (Page 3)

Part of the Section 4: Assembling and Deploying Applications domain, which makes up ~11% of our current practice bank. Databricks does not publish an official question count, but from its 90-minute exam (~35–60 total, ~4–7 in this domain), expect 1–2 from this objective — we provide 13 practice questions to prepare you well beyond it. (estimate)

13questions here

3free pages

5concepts

Questions 11–13

11foundation · easy
When assembling a RAG application in Databricks, which component is required to convert text chunks into numeric representations that can be compared for similarity?
Select an answer first
12application · medium
A RAG application is returning results from a Vector Search index, but the top results are not relevant to the user's query. The team suspects the query embedding is not aligned with the document embeddings. What is the most likely cause and fix?
Select an answer first
13foundation · easy
When querying a Vector Search index, what input is required to find relevant documents?
Select an answer first

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Build and Configure RAG Applications GENERATIVE-AI-ENGINEER-ASSOCIATE Practice Questions (Page 3)

Questions 11–13

When assembling a RAG application in Databricks, which component is required to convert text chunks into numeric representations that can be compared for similarity?

A RAG application is returning results from a Vector Search index, but the top results are not relevant to the user's query. The team suspects the query embedding is not aligned with the document embeddings. What is the most likely cause and fix?

When querying a Vector Search index, what input is required to find relevant documents?

Keep studying — Section 4: Assembling and Deploying Applications