Databricks Databricks Generative AI Engineer Associate Databricks Certified Generative AI Engineer Associate Online Training
Databricks Databricks Generative AI Engineer Associate Online Training
The questions for Databricks Generative AI Engineer Associate were last updated at Mar 11,2025.
- Exam Code: Databricks Generative AI Engineer Associate
- Exam Name: Databricks Certified Generative AI Engineer Associate
- Certification Provider: Databricks
- Latest update: Mar 11,2025
What is the most suitable library for building a multi-step LLM-based workflow?
- A . Pandas
- B . TensorFlow
- C . PySpark
- D . LangChain
When developing an LLM application, it’s crucial to ensure that the data used for training the model complies with licensing requirements to avoid legal risks.
Which action is NOT appropriate to avoid legal risks?
- A . Reach out to the data curators directly before you have started using the trained model to let them know.
- B . Use any available data you personally created which is completely original and you can decide what license to use.
- C . Only use data explicitly labeled with an open license and ensure the license terms are followed.
- D . Reach out to the data curators directly after you have started using the trained model to let them know.
A Generative AI Engineer is testing a simple prompt template in LangChain using the code below, but is getting an error.
Assuming the API key was properly defined, what change does the Generative AI Engineer need to make to fix their chain?
A)
B)
C)
D)
- A . Option A
- B . Option B
- C . Option C
- D . Option D
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?
- A . Split the LLM output by newline characters to truncate away the summarization explanation.
- B . Tune the chunk size of news articles or experiment with different embedding models.
- 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.
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?
- A . Add guardrails to filter outputs from the LLM before it is shown to the user
- B . Fine-tune the model on your data, hoping it will learn what is appropriate and not
- 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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