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

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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: 61 Q&A's Shared By: nuala
Question 12

A Generative Al Engineer is building an LLM-based application that has an

important transcription (speech-to-text) task. Speed is essential for the success of the application

Which open Generative Al models should be used?

Options:

A.

L!ama-2-70b-chat-hf

B.

MPT-30B-lnstruct

C.

DBRX

D.

whisper-large-v3 (1.6B)

Discussion
Question 13

Which indicator should be considered to evaluate the safety of the LLM outputs when qualitatively assessing LLM responses for a translation use case?

Options:

A.

The ability to generate responses in code

B.

The similarity to the previous language

C.

The latency of the response and the length of text generated

D.

The accuracy and relevance of the responses

Discussion
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Question 14

A Generative AI Engineer wants to build an LLM-based solution to help a restaurant improve its online customer experience with bookings by automatically handling common customer inquiries. The goal of the solution is to minimize escalations to human intervention and phone calls while maintaining a personalized interaction. To design the solution, the Generative AI Engineer needs to define the input data to the LLM and the task it should perform.

Which input/output pair will support their goal?

Options:

A.

Input: Online chat logs; Output: Group the chat logs by users, followed by summarizing each user’s interactions

B.

Input: Online chat logs; Output: Buttons that represent choices for booking details

C.

Input: Customer reviews; Output: Classify review sentiment

D.

Input: Online chat logs; Output: Cancellation options

Discussion
Question 15

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

Discussion
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