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Databricks-Generative-AI-Engineer-Associate Exam Dumps : Databricks Certified Generative AI Engineer Associate

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Databricks Certified Generative AI Engineer Associate Questions and Answers

Question 1

A Generative Al Engineer is creating an LLM-based application. The documents for its retriever have been chunked to a maximum of 512 tokens each. The Generative Al Engineer knows that cost and latency are more important than quality for this application. They have several context length levels to choose from.

Which will fulfill their need?

Options:

A.

context length 514; smallest model is 0.44GB and embedding dimension 768

B.

context length 2048: smallest model is 11GB and embedding dimension 2560

C.

context length 32768: smallest model is 14GB and embedding dimension 4096

D.

context length 512: smallest model is 0.13GB and embedding dimension 384

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Question 2

A Generative Al Engineer has created a RAG application to look up answers to questions about a series of fantasy novels that are being asked on the author’s web forum. The fantasy novel texts are chunked and embedded into a vector store with metadata (page number, chapter number, book title), retrieved with the user’s query, and provided to an LLM for response generation. The Generative AI Engineer used their intuition to pick the chunking strategy and associated configurations but now wants to more methodically choose the best values.

Which TWO strategies should the Generative AI Engineer take to optimize their chunking strategy and parameters? (Choose two.)

Options:

A.

Change embedding models and compare performance.

B.

Add a classifier for user queries that predicts which book will best contain the answer. Use this to filter retrieval.

C.

Choose an appropriate evaluation metric (such as recall or NDCG) and experiment with changes in the chunking strategy, such as splitting chunks by paragraphs or chapters.

Choose the strategy that gives the best performance metric.

D.

Pass known questions and best answers to an LLM and instruct the LLM to provide the best token count. Use a summary statistic (mean, median, etc.) of the best token counts to choose chunk size.

E.

Create an LLM-as-a-judge metric to evaluate how well previous questions are answered by the most appropriate chunk. Optimize the chunking parameters based upon the values of the metric.

Question 3

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.