Free Databricks Databricks-Generative-AI-Engineer-Associate Exam Dumps Questions & Answers

A Generative AI Engineer has created a RAG application which can help employees retrieve answers from an internal knowledge base, such as Confluence pages or Google Drive. The prototype application is now working with some positive feedback from internal company testers. Now the Generative Al Engineer wants to formally evaluate the system's performance and understand where to focus their efforts to further improve the system.
How should the Generative AI Engineer evaluate the system?

• A.  Curate a dataset that can test the retrieval and generation components of the system separately. Use MLflow's built in evaluation metrics to perform the evaluation on the retrieval and generation components.
• B.  Use cosine similarity score to comprehensively evaluate the quality of the final generated answers.
• C.  Benchmark multiple LLMs with the same data and pick the best LLM for the job.
• D.  Use an LLM-as-a-judge to evaluate the quality of the final answers generated.

A Generative Al Engineer is building an LLM-based application that has an important transcription (speech-to-text) task. Speed is essential for the success of the application Which open Generative Al models should be used?

• A.  DBRX
• B.  MPT-30B-lnstruct
• C.  L!ama-2-70b-chat-hf
• D.  whisper-large-v3 (1.6B)

A Generative Al Engineer is tasked with developing a RAG application that will help a small internal group of experts at their company answer specific questions, augmented by an internal knowledge base. They want the best possible quality in the answers, and neither latency nor throughput is a huge concern given that the user group is small and they're willing to wait for the best answer. The topics are sensitive in nature and the data is highly confidential and so, due to regulatory requirements, none of the information is allowed to be transmitted to third parties.
Which model meets all the Generative Al Engineer's needs in this situation?

• A.  BGE-large
• B.  Llama2-70B
• C.  Dolly 1.5B
• D.  OpenAI GPT-4

A Generative AI Engineer received the following business requirements for an external chatbot.
The chatbot needs to know what types of questions the user asks and routes to appropriate models to answer the questions. For example, the user might ask about upcoming event details. Another user might ask about purchasing tickets for a particular event.
What is an ideal workflow for such a chatbot?

• A.  The chatbot should only look at previous event information
• B.  The chatbot should only process payments
• C.  There should be two different chatbots handling different types of user queries.
• D.  The chatbot should be implemented as a multi-step LLM workflow. First, identify the type of question asked, then route the question to the appropriate model. If it's an upcoming event question, send the query to a text-to-SQL model. If it's about ticket purchasing, the customer should be redirected to a payment platform.

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.  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.
• B.  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.
• C.  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.
• 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.