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databricks CERTIFIED_GENERATIVE_AI_ENGINEER_ASSOCIATE

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Exam contains 56 questions

Page 8 of 10
Question 43 🔥

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?

Which database solution meets these requirements?
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Question 44 🔥

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?

Which database solution meets these requirements?
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Question 45 🔥

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?

Which database solution meets these requirements?
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Question 46 🔥

A Generative AI Engineer has written scalable PySpark code to ingest unstructured PDF documents and chunk them in preparation for storing in a Databricks Vector Search index. Currently, the two columns of their dataframe include the original filename as a string and an array of text chunks from that document.What set of steps should the Generative AI Engineer perform to store the chunks in a ready-to-ingest manner for Databricks Vector Search?

Which database solution meets these requirements?
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Question 47 🔥

A Generative AI Engineer is ready to deploy an LLM application written using Foundation Model APIs. They want to follow security best practices for production scenarios.Which authentication method should they choose?

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Question 48 🔥

A Generative AI Engineer is developing a RAG system for their company to perform internal document Q&A for structured HR policies, but the answers returned are frequently incomplete and unstructured. It seems that the retriever is not returning all relevant context. The Generative AI Engineer has experimented with different embedding and response generating LLMs but that did not improve results.Which TWO options could be used to improve the response quality? (Choose two.)

Which database solution meets these requirements?
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