Databricks Certified Generative AI Engineer Associate Practice Test
Build your confidence for Databricks Certified Generative AI Engineer Associate. Practice the concepts, understand the answers, and strengthen your knowledge one question at a time.
Try a sample questionExam overview and details
The Databricks Certified Generative AI Engineer Associate certification validates the technical proficiency required to design, build, and deploy production-grade generative AI applications on the Databricks Data Intelligence Platform. This credential demonstrates a practitioner's ability to leverage the full Databricks stack-including Databricks Runtime for Machine Learning, MLflow, Vector Search, and the Databricks AI Model Serving infrastructure-to operationalize generative AI solutions. Certified professionals are skilled in integrating large language models (LLMs) with enterprise data, implementing retrieval-augmented generation (RAG) architectures, managing model lifecycle, and ensuring applications are scalable, secure, and performant. Earning this certification signals to employers a concrete, vendor-validated competency in one of the most transformative and in-demand technology domains, positioning the holder as a key contributor to strategic AI initiatives that drive business innovation and competitive advantage.
Sample Questions
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A Generative AI Engineer is preparing a Databricks Generative AI solution: making a validated chain available to a web application for a service entitlement assistant with Databricks notebooks, MLflow, and governed data. The app needs a REST API with scaling. The decision concerns serving endpoint for a service entitlement assistant. Which option is the best next step?
In a Databricks production-readiness review, the team describes this situation: helping a product team launch Q&A over curated release notes and implementation guides. The first version needs grounded answers with citations and no external actions. The engineer is deciding how to handle Knowledge Assistant scope. Which approach best satisfies the requirement?
A Databricks GenAI application is being designed for this workflow: four reviewers rate the same RAG answers differently. The team needs stable evaluation data for iteration. The design question is SME rubric use. What should the engineer recommend?
a team confuses evaluation and monitoring for a RAG app for an HR benefits assistant with Databricks notebooks, MLflow, and governed data. They ask when to run offline judges versus production inference logs. The issue centers on lifecycle phase for an HR benefits assistant.
Which choice is most appropriate?
comparing prompt templates, retriever settings, and model choices. The team needs reproducible metrics and artifacts. The issue centers on MLflow experiments.
Which choice is most appropriate?
Career Opportunities & Salary
Exam insights and study advice
In the rapidly evolving landscape of artificial intelligence, this certification provides a critical differentiator for technology professionals. It offers formal, industry-recognized validation of hands-on skills with the Databricks platform, which is a leading enterprise environment for generative AI development. Achieving this certification enhances career credibility, increases marketability for roles such as AI Engineer, ML Engineer, and Solutions Architect, and demonstrates a commitment to mastering cutting-edge tools. For organizations, it helps identify talent capable of turning generative AI prototypes into reliable, value-generating applications, thereby de-risking AI investments and accelerating time-to-value for AI-powered products and services.
What this exam covers
Use the published domain weights to plan your study. Practice results do not predict your certification exam score.