Newest Databricks-Generative-AI-Engineer-Associate Practice Questions at Zero Cost

Get a glimpse of the real Databricks-Generative-AI-Engineer-Associate certification exam challenges with our free Databricks-Generative-AI-Engineer-Associate practice test questions.

A Generative AI Engineer is developing a patient-facing healthcare-focused chatbot. If the patient's question is not a medical emergency, the chatbot should solicit more information from the patient to pass to the doctor's office and suggest a few relevant pre-approved medical articles for reading. If the patient's question is urgent, direct the patient to calling their local emergency services.

Given the following user input:

''I have been experiencing severe headaches and dizziness for the past two days.''

Which response is most appropriate for the chatbot to generate?

AHere are a few relevant articles for your browsing. Let me know if you have questions after reading them.

BPlease call your local emergency services.

CHeadaches can be tough. Hope you feel better soon!

DPlease provide your age, recent activities, and any other symptoms you have noticed along with your headaches and dizziness.

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

A Generative AI Engineer is developing an LLM application that users can use to generate personalized birthday poems based on their names.

Which technique would be most effective in safeguarding the application, given the potential for malicious user inputs?

AImplement a safety filter that detects any harmful inputs and ask the LLM to respond that it is unable to assist

BReduce the time that the users can interact with the LLM

CAsk the LLM to remind the user that the input is malicious but continue the conversation with the user

DIncrease the amount of compute that powers the LLM to process input faster

What is an effective method to preprocess prompts using custom code before sending them to an LLM?

ADirectly modify the LLM's internal architecture to include preprocessing steps

BIt is better not to introduce custom code to preprocess prompts as the LLM has not been trained with examples of the preprocessed prompts

CRather than preprocessing prompts, it's more effective to postprocess the LLM outputs to align the outputs to desired outcomes

DWrite a MLflow PyFunc model that has a separate function to process the prompts

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?

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

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

CInput: Customer reviews; Output: Classify review sentiment

DInput: Online chat logs; Output: Cancellation options

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