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Databricks Updated Databricks-Generative-AI-Engineer-Associate Exam Questions and Answers by ayzel

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Databricks Databricks-Generative-AI-Engineer-Associate Exam Overview :

Exam Name: Databricks Certified Generative AI Engineer Associate
Exam Code: Databricks-Generative-AI-Engineer-Associate Dumps
Vendor: Databricks Certification: Generative AI Engineer
Questions: 90 Q&A's Shared By: ayzel
Question 8

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.

Discussion
Question 9

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.

Discussion
Question 10

A Generative AI Engineer is deploying a customer-facing, fine-tuned LLM on their public website. Given the large investment the company put into fine-tuning this model, and the proprietary nature of the tuning data, they are concerned about model inversion attacks. Which of the following Databricks AI Security Framework (DASF) risk mitigation strategies are most relevant to this use case?

Options:

A.

Implement AI guardrails to allow users to configure and enforce compliance

B.

Leverage Databricks access control lists (ACLs) to configure permissions for accessing models

C.

Use secure model features with Databricks Feature Store

D.

Apply attribute-based access controls (ABAC) to limit unauthorized access

Discussion
Andrew
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Question 11

A Generative Al Engineer is working with a retail company that wants to enhance its customer experience by automatically handling common customer inquiries. They are working on an LLM-powered Al solution that should improve response times while maintaining a personalized interaction. They want to define the appropriate input and LLM task to do this.

Which input/output pair will do this?

Options:

A.

Input: Customer reviews; Output Group the reviews by users and aggregate per-user average rating, then respond

B.

Input: Customer service chat logs; Output Group the chat logs by users, followed by summarizing each user ' s interactions, then respond

C.

Input: Customer service chat logs; Output: Find the answers to similar questions and respond with a summary

D.

Input: Customer reviews: Output Classify review sentiment

Discussion
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