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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
After changing the response generating LLM in a RAG pipeline from GPT-4 to a model with a shorter context length that the company self-hosts, the Generative AI Engineer is getting the following error:

What TWO solutions should the Generative AI Engineer implement without changing the response generating model? (Choose two.)
Select 2
Options
A Retrain the response generating model using ALiBi
B Reduce the maximum output tokens of the new model
C Use a smaller embedding model to generate
D Decrease the chunk size of embedded documents
E Reduce the number of records retrieved from the vector database
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2
Question
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 service chat logs; Output Group the chat logs by users, followed by summarizing each user's interactions, then respond
B Input: Customer reviews; Output Group the reviews by users and aggregate per-user average rating, 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
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3
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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4
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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5
Question
A Generative Al Engineer is creating an LLM system that will retrieve news articles from the year 1918 and related to a user's query and summarize them. The engineer has noticed that the summaries are generated well but often also include an explanation of how the summary was generated, which is undesirable.

Which change could the Generative Al Engineer perform to mitigate this issue?
Options
A Tune the chunk size of news articles or experiment with different embedding models.
B Split the LLM output by newline characters to truncate away the summarization explanation.
C Revisit their document ingestion logic, ensuring that the news articles are being ingested properly.
D Provide few shot examples of desired output format to the system and/or user prompt.
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