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Databricks-Generative-AI-Engineer-Associate Questions and Answers

Question # 1

A small and cost-conscious startup in the cancer research field wants to build a RAG application using Foundation Model APIs.

Which strategy would allow the startup to build a good-quality RAG application while being cost-conscious and able to cater to customer needs?

A.

Limit the number of relevant documents available for the RAG application to retrieve from

B.

Pick a smaller LLM that is domain-specific

C.

Limit the number of queries a customer can send per day

D.

Use the largest LLM possible because that gives the best performance for any general queries

Question # 2

A Generative Al Engineer is setting up a Databricks Vector Search that will lookup news articles by topic within 10 days of the date specified An example query might be "Tell me about monster truck news around January 5th 1992". They want to do this with the least amount of effort.

How can they set up their Vector Search index to support this use case?

A.

Split articles by 10 day blocks and return the block closest to the query.

B.

Include metadata columns for article date and topic to support metadata filtering.

C.

pass the query directly to the vector search index and return the best articles.

D.

Create separate indexes by topic and add a classifier model to appropriately pick the best index.

Question # 3

A Generative Al Engineer has created a RAG application to look up answers to questions about a series of fantasy novels that are being asked on the author’s web forum. The fantasy novel texts are chunked and embedded into a vector store with metadata (page number, chapter number, book title), retrieved with the user’s query, and provided to an LLM for response generation. The Generative AI Engineer used their intuition to pick the chunking strategy and associated configurations but now wants to more methodically choose the best values.

Which TWO strategies should the Generative AI Engineer take to optimize their chunking strategy and parameters? (Choose two.)

A.

Change embedding models and compare performance.

B.

Add a classifier for user queries that predicts which book will best contain the answer. Use this to filter retrieval.

C.

Choose an appropriate evaluation metric (such as recall or NDCG) and experiment with changes in the chunking strategy, such as splitting chunks by paragraphs or chapters.

Choose the strategy that gives the best performance metric.

D.

Pass known questions and best answers to an LLM and instruct the LLM to provide the best token count. Use a summary statistic (mean, median, etc.) of the best token counts to choose chunk size.

E.

Create an LLM-as-a-judge metric to evaluate how well previous questions are answered by the most appropriate chunk. Optimize the chunking parameters based upon the values of the metric.

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