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

Question 7

What is an effective method to preprocess prompts using custom code before sending them to an LLM?

A. Directly modify the LLM??s internal architecture to include preprocessing steps

B. It is better not to introduce custom code to preprocess prompts as the LLM has not been trained with examples of the preprocessed prompts

C. Rather than preprocessing prompts, it??s more effective to postprocess the LLM outputs to align the outputs to desired outcomes

D. Write a MLflow PyFunc model that has a separate function to process the prompts

Correct Answer:D

Question 8

A Generative AI Engineer wants to build an LLM-based solution to help a restaurant improve its online customer experience with bookings by automatically handling common customer inquiries. The goal of the solution is to minimize escalations to human intervention and phone calls while maintaining a personalized interaction. To design the solution, the Generative AI Engineer needs to define the input data to the LLM and the task it should perform.
Which input/output pair will support their goal?

A. Input: Online chat logs; Output: Group the chat logs by users, followed by summarizing each user??s interactions

B. Input: Online chat logs; Output: Buttons that represent choices for booking details

C. Input: Customer reviews; Output: Classify review sentiment

D. Input: Online chat logs; Output: Cancellation options

Correct Answer:B

Question 9

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.

Correct Answer:B

Question 10

A Generative Al Engineer interfaces with an LLM with prompt/response behavior that has been trained on customer calls inquiring about product availability. The LLM is designed to output ??In Stock?? if the product is available or only the term ??Out of Stock?? if not.
Which prompt will work to allow the engineer to respond to call classification labels correctly?

A. Respond with ??In Stock?? if the customer asks for a product.

B. You will be given a customer call transcript where the customer asks about product availabilit

C. The outputs are either ??In Stock?? or ??Out of Stock??. Format the output in JSON, for example: {??call_id??: ??123??, ??label??: ??In Stock??}.

D. Respond with ??Out of Stock?? if the customer asks for a product.

E. You will be given a customer call transcript where the customer inquires about product availabilit

F. Respond with ??In Stock?? if the product is available or ??Out of Stock?? if not.

Correct Answer:B

Question 11

A Generative Al Engineer is tasked with developing an application that is based on an open source large language model (LLM). They need a foundation LLM with a large context window.
Which model fits this need?

A. DistilBERT

B. MPT-30B

C. Llama2-70B

D. DBRX

Correct Answer:C

Question 12

A Generative Al Engineer is creating an LLM-based application. The documents for its retriever have been chunked to a maximum of 512 tokens each. The Generative Al Engineer knows that cost and latency are more important than quality for this application. They have several context length levels to choose from.
Which will fulfill their need?

A. context length 514; smallest model is 0.44GB and embedding dimension 768

B. context length 2048: smallest model is 11GB and embedding dimension 2560

C. context length 32768: smallest model is 14GB and embedding dimension 4096

D. context length 512: smallest model is 0.13GB and embedding dimension 384

Correct Answer:D

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