Online Access Free Databricks-Generative-AI-Engineer-Associate Practice Test

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?

• A.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.
• B.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.
• C.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.
• D.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.

A Generative AI Engineer is building a RAG application that will rely on context retrieved from source documents that are currently in PDF format. These PDFs can contain both text and images. They want to develop a solution using the least amount of lines of code.
Which Python package should be used to extract the text from the source documents?

• A.beautifulsoup
• B.flask
• C.numpy
• D.unstructured

A Generative Al Engineer is building a production-ready LLM system which replies directly to customers.
The solution makes use of the Foundation Model API via provisioned throughput. They are concerned that the LLM could potentially respond in a toxic or otherwise unsafe way. They also wish to perform this with the least amount of effort.
Which approach will do this?

• A.Add some LLM calls to their chain to detect unsafe content before returning text
• B.Ask users to report unsafe responses
• C.Host Llama Guard on Foundation Model API and use it to detect unsafe responses
• D.Add a regex expression on inputs and outputs to detect unsafe responses.

When developing an LLM application, it's crucial to ensure that the data used for training the model complies with licensing requirements to avoid legal risks.
Which action is NOT appropriate to avoid legal risks?

• A.Reach out to the data curators directly before you have started using the trained model to let them know.
• B.Only use data explicitly labeled with an open license and ensure the license terms are followed.
• C.Reach out to the data curators directly after you have started using the trained model to let them know.
• D.Use any available data you personally created which is completely original and you can decide what license to use.

A Generative Al Engineer is using an LLM to classify species of edible mushrooms based on text descriptions of certain features. The model is returning accurate responses in testing and the Generative Al Engineer is confident they have the correct list of possible labels, but the output frequently contains additional reasoning in the answer when the Generative Al Engineer only wants to return the label with no additional text.
Which action should they take to elicit the desired behavior from this LLM?

• A.Use few snot prompting to instruct the model on expected output format
• B.Use zero shot prompting to instruct the model on expected output format
• C.Use a system prompt to instruct the model to be succinct in its answer
• D.Use zero shot chain-of-thought prompting to prevent a verbose output format