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Databricks Updated Databricks-Generative-AI-Engineer-Associate Exam Questions and Answers by jayce

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Databricks Databricks-Generative-AI-Engineer-Associate Exam Overview :

Exam Name: Databricks Certified Generative AI Engineer Associate
Exam Code: Databricks-Generative-AI-Engineer-Associate Dumps
Vendor: Databricks Certification: Generative AI Engineer
Questions: 61 Q&A's Shared By: jayce
Question 16

A Generative Al Engineer is developing a RAG system for their company to perform internal document Q&A for structured HR policies, but the answers returned are frequently incomplete and unstructured It seems that the retriever is not returning all relevant context The Generative Al Engineer has experimented with different embedding and response generating LLMs but that did not improve results.

Which TWO options could be used to improve the response quality?

Choose 2 answers

Options:

A.

Add the section header as a prefix to chunks

B.

Increase the document chunk size

C.

Split the document by sentence

D.

Use a larger embedding model

E.

Fine tune the response generation model

Discussion
Question 17

A Generative Al Engineer has successfully ingested unstructured documents and chunked them by document sections. They would like to store the chunks in a Vector Search index. The current format of the dataframe has two columns: (i) original document file name (ii) an array of text chunks for each document.

What is the most performant way to store this dataframe?

Options:

A.

Split the data into train and test set, create a unique identifier for each document, then save to a Delta table

B.

Flatten the dataframe to one chunk per row, create a unique identifier for each row, and save to a Delta table

C.

First create a unique identifier for each document, then save to a Delta table

D.

Store each chunk as an independent JSON file in Unity Catalog Volume. For each JSON file, the key is the document section name and the value is the array of text chunks for that section

Discussion
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Question 18

A Generative AI Engineer has been asked to design an LLM-based application that accomplishes the following business objective: answer employee HR questions using HR PDF documentation.

Which set of high level tasks should the Generative AI Engineer's system perform?

Options:

A.

Calculate averaged embeddings for each HR document, compare embeddings to user query to find the best document. Pass the best document with the user query into an LLM with a large context window to generate a response to the employee.

B.

Use an LLM to summarize HR documentation. Provide summaries of documentation and user query into an LLM with a large context window to generate a response to the user.

C.

Create an interaction matrix of historical employee questions and HR documentation. Use ALS to factorize the matrix and create embeddings. Calculate the embeddings of new queries and use them to find the best HR documentation. Use an LLM to generate a response to the employee question based upon the documentation retrieved.

D.

Split HR documentation into chunks and embed into a vector store. Use the employee question to retrieve best matched chunks of documentation, and use the LLM to generate a response to the employee based upon the documentation retrieved.

Discussion
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