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Databricks Certified Generative AI Engineer Associate

Last Update Oct 14, 2024
Total Questions : 45 With Methodical Explanation

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Total Questions : 45

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Databricks Certified Generative AI Engineer Associate Questions and Answers

Questions 1

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?

Options:

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

C.

Respond with “Out of Stock” if the customer asks for a product.

D.

You will be given a customer call transcript where the customer inquires about product availability. Respond with “In Stock” if the product is available or “Out of Stock” if not.

Questions 2

A Generative Al Engineer is building a RAG application that answers questions about internal documents for the company SnoPen AI.

The source documents may contain a significant amount of irrelevant content, such as advertisements, sports news, or entertainment news, or content about other companies.

Which approach is advisable when building a RAG application to achieve this goal of filtering irrelevant information?

Options:

A.

Keep all articles because the RAG application needs to understand non-company content to avoid answering questions about them.

B.

Include in the system prompt that any information it sees will be about SnoPenAI, even if no data filtering is performed.

C.

Include in the system prompt that the application is not supposed to answer any questions unrelated to SnoPen Al.

D.

Consolidate all SnoPen AI related documents into a single chunk in the vector database.

Questions 3

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.