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Databricks Databricks-Generative-AI-Engineer-Associate Exam With Confidence Using Practice Dumps

Exam Code:
Databricks-Generative-AI-Engineer-Associate
Exam Name:
Databricks Certified Generative AI Engineer Associate
Certification:
Vendor:
Questions:
45
Last Updated:
Nov 21, 2024
Exam Status:
Stable
Databricks Databricks-Generative-AI-Engineer-Associate

Databricks-Generative-AI-Engineer-Associate: Generative AI Engineer Exam 2024 Study Guide Pdf and Test Engine

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Databricks Certified Generative AI Engineer Associate Questions and Answers

Question 1

A company has a typical RAG-enabled, customer-facing chatbot on its website.

Select the correct sequence of components a user's questions will go through before the final output is returned. Use the diagram above for reference.

Options:

A.

1.embedding model, 2.vector search, 3.context-augmented prompt, 4.response-generating LLM

B.

1.context-augmented prompt, 2.vector search, 3.embedding model, 4.response-generating LLM

C.

1.response-generating LLM, 2.vector search, 3.context-augmented prompt, 4.embedding model

D.

1.response-generating LLM, 2.context-augmented prompt, 3.vector search, 4.embedding model

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Question 2

A small and cost-conscious startup in the cancer research field wants to build a RAG application using Foundation Model APIs.

Which strategy would allow the startup to build a good-quality RAG application while being cost-conscious and able to cater to customer needs?

Options:

A.

Limit the number of relevant documents available for the RAG application to retrieve from

B.

Pick a smaller LLM that is domain-specific

C.

Limit the number of queries a customer can send per day

D.

Use the largest LLM possible because that gives the best performance for any general queries

Question 3

Generative AI Engineer at an electronics company just deployed a RAG application for customers to ask questions about products that the company carries. However, they received feedback that the RAG response often returns information about an irrelevant product.

What can the engineer do to improve the relevance of the RAG’s response?

Options:

A.

Assess the quality of the retrieved context

B.

Implement caching for frequently asked questions

C.

Use a different LLM to improve the generated response

D.

Use a different semantic similarity search algorithm