Databricks-Generative-AI-Engineer-Associate Databricks Exam Questions and Free Practice Test

Question 13

A Generative Al Engineer at an automotive company would like to build a question- answering chatbot for customers to inquire about their vehicles. They have a database containing various documents of different vehicle makes, their hardware parts, and common maintenance information.
Which of the following components will NOT be useful in building such a chatbot?

A. Response-generating LLM

B. Invite users to submit long, rather than concise, questions

C. Vector database

D. Embedding model

Correct Answer:B

Question 14

A Generative AI Engineer is creating an LLM-powered application that will need access to up-to-date news articles and stock prices.
The design requires the use of stock prices which are stored in Delta tables and finding the latest relevant news articles by searching the internet.
How should the Generative AI Engineer architect their LLM system?

A. Use an LLM to summarize the latest news articles and lookup stock tickers from the summaries to find stock prices.

B. Query the Delta table for volatile stock prices and use an LLM to generate a search query to investigate potential causes of the stock volatility.

C. Download and store news articles and stock price information in a vector stor

D. Use a RAG architecture to retrieve and generate at runtime.

E. Create an agent with tools for SQL querying of Delta tables and web searching, provide retrieved values to an LLM for generation of response.

Correct Answer:D

Question 15

A Generative AI Engineer just deployed an LLM application at a digital marketing company that assists with answering customer service inquiries.
Which metric should they monitor for their customer service LLM application in production?

A. Number of customer inquiries processed per unit of time

B. Energy usage per query

C. Final perplexity scores for the training of the model

D. HuggingFace Leaderboard values for the base LLM

Correct Answer:A

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

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

Correct Answer:AB

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