Expert Answers to Databricks Exam Databricks-Generative-AI-Engineer-Associate Questions

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Generative AI Engineer Databricks Certified Generative AI Engineer Associate

Databricks Certified Generative AI Engineer Associate

Last Update Oct 8, 2026
Total Questions : 90

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Questions 2

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:

Split the LLM output by newline characters to truncate away the summarization explanation.

Tune the chunk size of news articles or experiment with different embedding models.

Revisit their document ingestion logic, ensuring that the news articles are being ingested properly.

Provide few shot examples of desired output format to the system and/or user prompt.

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Questions 3

A Generative AI Engineer I using the code below to test setting up a vector store:

Questions 3

Assuming they intend to use Databricks managed embeddings with the default embedding model, what should be the next logical function call?

Options:

vsc.get_index()

vsc.create_delta_sync_index()

vsc.create_direct_access_index()

vsc.similarity_search()

Discussion 0

Questions 4

A Generative Al Engineer is tasked with developing an application that is based on an open source large language model (LLM). They need a foundation LLM with a large context window.

Which model fits this need?

Options:

DistilBERT

MPT-30B

Llama2-70B

DBRX

Discussion 0

Questions 5

A generative AI engineer is deploying an AI agent authored with MLflow’s ChatAgent interface for a retail company ' s customer support system on Databricks. The agent must handle thousands of inquiries daily, and the engineer needs to track its performance and quality in real-time to ensure it meets service-level agreements. Which metrics are automatically captured by default and made available for monitoring when the agent is deployed using the Mosaic AI Agent Framework?

Options:

Operational metrics like request volume, latency, and errors

Quality metrics like correctness and guideline adherence

Both operational and quality metrics

No metrics are automatically captured

Discussion 0

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