Exam Code: Databricks-Generative-AI-Engineer-Associate
Exam Name: Databricks Certified Generative AI Engineer Associate
Certification Provider: Databricks
Corresponding Certification: Generative AI Engineer

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Databricks Databricks-Generative-AI-Engineer-Associate Exam Syllabus Topics:

Topic	Details
Topic 1	Data Preparation: Generative AI Engineers covers a chunking strategy for a given document structure and model constraints. The topic also focuses on filter extraneous content in source documents. Lastly, Generative AI Engineers also learn about extracting document content from provided source data and format.
Topic 2	Assembling and Deploying Applications: In this topic, Generative AI Engineers get knowledge about coding a chain using a pyfunc mode, coding a simple chain using langchain, and coding a simple chain according to requirements. Additionally, the topic focuses on basic elements needed to create a RAG application. Lastly, the topic addresses sub-topics about registering the model to Unity Catalog using MLflow.
Topic 3	Evaluation and Monitoring: This topic is all about selecting an LLM choice and key metrics. Moreover, Generative AI Engineers learn about evaluating model performance. Lastly, the topic includes sub-topics about inference logging and usage of Databricks features.

Reference: https://www.databricks.com/learn/certification/genai-engineer-associate

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Databricks Certified Generative AI Engineer Associate Sample Questions:

Question #1

A Generative AI Engineer is evaluating a customer-support agent in Databricks. The team needs to score each response on a domain-specific policy: the answer must cite an approved refund rule and must not mention unsupported escalation paths. Built-in evaluation metrics do not capture this logic. The team wants the metric to run during agent evaluation in Databricks and return a repeatable, structured score for each trace.
Which approach should the engineer use?

A. Use only latency and token-count metrics because custom policy checks are not supported in evaluation workflows.
B. Log the traces to MLflow v3.x and review failures in the UI without defining a scorer.
C. Add the policy text to the system prompt and rely on the model's self-reported compliance as the evaluation result.
D. Create a custom MLflow scorer that inspects agent outputs against the policy and pass it into the evaluation run.

Reveal Solution Discussion 0

Correct Answer: D 🗳️