Databricks Certified Generative AI Engineer Associate : Databricks-Generative-AI-Engineer-Associate

Exam Code: Databricks-Generative-AI-Engineer-Associate
Exam Name: Databricks Certified Generative AI Engineer Associate
Updated: Oct 10, 2026
Q&As: 94 Questions and Answers

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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	Governance: Generative AI Engineers who take the exam get knowledge about masking techniques, guardrail techniques, and legal licensing requirements in this topic.
Topic 3	Design Applications: The topic focuses on designing a prompt that elicits a specifically formatted response. It also focuses on selecting model tasks to accomplish a given business requirement. Lastly, the topic covers chain components for a desired model input and output.
Topic 4	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.

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

Databricks Certified Generative AI Engineer Associate Sample Questions:

Question #1

A Generative AI Engineer is deploying a customer-facing, fine-tuned LLM on their public website. Given the large investment the company put into fine-tuning this model, and the proprietary nature of the tuning data, they are concerned about model inversion attacks. Which of the following Databricks AI Security Framework (DASF) risk mitigation strategies are most relevant to this use case?

A. Apply attribute-based access controls (ABAC) to limit unauthorized access
B. Implement AI guardrails to allow users to configure and enforce compliance
C. Use secure model features with Databricks Feature Store
D. Leverage Databricks access control lists (ACLs) to configure permissions for accessing models

Reveal Solution Discussion 0

Correct Answer: B 🗳️