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Generative AI Engineer Associate (Certified Generative AI Engineer Associate) - Databricks Exam Questions

Last updated on June 20, 2026

97% Exam Compliance
73 Total Questions
1
Question
All of the following are Python APIs used to query Databricks foundation models. When running in an interactive notebook, which of the following libraries does not automatically use the current session credentials?
Options
A REST API via requests library
B OpenAI client
C MLflow Deployments SDK
D Databricks Python SDK
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2
Question
What is an effective method to preprocess prompts using custom code before sending them to an LLM?
Options
A It is better not to introduce custom code to preprocess prompts as the LLM has not been trained with examples of the preprocessed prompts
B Directly modify the LLM’s internal architecture to include preprocessing steps
C Rather than preprocessing prompts, it’s more effective to postprocess the LLM outputs to align the outputs to desired outcomes
D Write a MLflow PyFunc model that has a separate function to process the prompts
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3
Question
A Generative AI Engineer is developing a chatbot designed to assist users with insurance-related queries. The chatbot is built on a large language model (LLM) and is conversational. However, to maintain the chatbot’s focus and to comply with company policy, it must not provide responses to questions about politics. Instead, when presented with political inquiries, the chatbot should respond with a standard message: “Sorry, I cannot answer that. I am a chatbot that can only answer questions around insurance.”

Which framework type should be implemented to solve this?
Options
A Security Guardrail
B Safety Guardrail
C Contextual Guardrail
D Compliance Guardrail
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4
Question
A Generative Al Engineer has developed an LLM application to answer questions about internal company policies. The Generative AI Engineer must ensure that the application doesn’t hallucinate or leak confidential data.

Which approach should NOT be used to mitigate hallucination or confidential data leakage?
Options
A Fine-tune the model on your data, hoping it will learn what is appropriate and not
B Add guardrails to filter outputs from the LLM before it is shown to the user
C Limit the data available based on the user’s access level
D Use a strong system prompt to ensure the model aligns with your needs.
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5
Question
A Generative AI Engineer is tasked with deploying an application that takes advantage of a custom MLflow Pyfunc model to return some interim results.

How should they configure the endpoint to pass the secrets and credentials?
Options
A Pass variables using the Databricks Feature Store API
B Use spark.conf.set ()
C Add credentials using environment variables
D Pass the secrets in plain text
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