Databricks-Generative-AI-Engineer-Associate 무료 덤프문제 온라인 액세스

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.

A Generative Al Engineer has built an LLM-based system that will automatically translate user text between two languages. They now want to benchmark multiple LLM's on this task and pick the best one. They have an evaluation set with known high quality translation examples. They want to evaluate each LLM using the evaluation set with a performant metric.
Which metric should they choose for this evaluation?

• A.BLEU metric
• B.ROUGE metric
• C.NDCG metric
• D.RECALL metric

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.Embedding model
• C.Invite users to submit long, rather than concise, questions
• D.Vector database

A Generative Al Engineer has already trained an LLM on Databricks and it is now ready to be deployed.
Which of the following steps correctly outlines the easiest process for deploying a model on Databricks?

• A.Wrap the LLM's prediction function into a Flask application and serve using Gunicorn
• B.Log the model using MLflow during training, directly register the model to Unity Catalog using the MLflow API, and start a serving endpoint
• C.Log the model as a pickle object, upload the object to Unity Catalog Volume, register it to Unity Catalog using MLflow, and start a serving endpoint
• D.Save the model along with its dependencies in a local directory, build the Docker image, and run the Docker container

A Generative AI Engineer has been asked to build an LLM-based question-answering application. The application should take into account new documents that are frequently published. The engineer wants to build this application with the least cost and least development effort and have it operate at the lowest cost possible.
Which combination of chaining components and configuration meets these requirements?

• A.For the application a prompt, an agent and a fine-tuned LLM are required. The agent is used by the LLM to retrieve relevant content that is inserted into the prompt which is given to the LLM to generate answers.
• B.For the application a prompt, a retriever, and an LLM are required. The retriever output is inserted into the prompt which is given to the LLM to generate answers.
• C.The LLM needs to be frequently with the new documents in order to provide most up-to-date answers.
• D.For the question-answering application, prompt engineering and an LLM are required to generate answers.