Databricks Certified Generative AI Engineer Associate Practice Tests

Application Development is the heaviest domain at 30%, followed by Assembling and Deploying Applications at 22%, Design Applications at 14%, Data Preparation at 14%, Evaluation and Monitoring at 12%, and Governance at 8%. This 620-question practice bank is built to match that split, so LLM chain construction, prompt augmentation, and Model Serving deployment scenarios get real depth instead of a token appearance. Watch the freshness of whatever you study from: Databricks refreshed the exam guide on March 18, 2026, and the live exam now covers Agent Bricks, MCP server integration, AI Gateway, Genie Spaces, and ai_query() batch inference alongside the classic RAG material.

Test day means 45 scored multiple-choice and multiple-select questions in 90 minutes, delivered as an online proctored exam through Webassessor. Databricks may mix in unscored pilot questions that are not identified and do not affect your result, with extra time already factored in to cover them. There is no published passing percentage; you receive a pass or fail outcome with a per-domain breakdown, and candidates commonly target roughly 70% to be safe. All ML code on the exam is written in Python, SQL can appear for non-ML data manipulation, and the exam is offered in English, Japanese, Korean, and Brazilian Portuguese.

There are no formal prerequisites, but Databricks recommends six months of hands-on generative AI work plus the Generative AI Engineering with Databricks courses in Databricks Academy. Registration costs $200 USD per attempt, and the credential expires after two years; recertifying means retaking the full exam that is live at that time, which matters here because the content shifts fast. Start with the 30 free questions, then work through the full 620-question bank until your accuracy holds steady across all six domains.

The Databricks Certified Generative AI Engineer Associate certification validates an individual's ability to design, develop, and deploy large language model (LLM)-powered solutions on the Databricks platform. The exam tests practical competency across the full generative AI engineering lifecycle, including decomposing complex requirements into multi-stage reasoning pipelines, selecting appropriate models from both open-source and proprietary ecosystems, and implementing retrieval-augmented generation (RAG) applications using Databricks-native tooling.

Certified professionals are expected to demonstrate hands-on proficiency with key Databricks technologies: Vector Search for semantic similarity and document retrieval, Model Serving for scalable endpoint deployment, MLflow for experiment tracking and lifecycle management, and Unity Catalog for data governance and access control. All machine learning code on the exam is in Python; SQL may appear for non-ML data manipulation tasks. The certification is valid for two years, after which recertification requires retaking the current version of the exam.

This certification is designed for practitioners actively building and deploying AI systems in enterprise environments, including AI Engineers, Generative AI Engineers, LLM Engineers, AI Solution Architects, MLOps Engineers, and Data Scientists with a focus on LLM or RAG workflows. It is particularly well-suited for engineers who work within the Databricks ecosystem and need to demonstrate production-level competency in generative AI solution development.

Candidates should have at least six months of hands-on experience developing generative AI solutions, practical familiarity with Python-based ML pipelines, and working knowledge of frameworks such as LangChain or LangGraph. Experience with Databricks-specific tools—MLflow, Unity Catalog, Vector Search, and Model Serving—is strongly recommended before attempting the exam.

There are no formal prerequisites required to register for this exam; any candidate may attempt it. However, Databricks strongly recommends at least six months of hands-on experience in generative AI solution development before sitting for the certification.

Recommended technical knowledge includes Python proficiency (especially for model pipelines and application orchestration), familiarity with LLM concepts such as context windows, tokenization, and prompt engineering techniques (zero-shot, few-shot, chain-of-thought), and practical experience with LangChain or similar orchestration frameworks. Candidates should also be comfortable using Databricks-native tools including MLflow for experiment tracking, Unity Catalog for governance, Vector Search for embedding-based retrieval, and Model Serving for endpoint deployment.

The exam consists of approximately 45 scored multiple-choice and multiple-select questions to be completed within 90 minutes. It is delivered as a proctored online exam, meaning candidates complete it remotely under live or automated proctoring; no external aids are permitted. The exam is available in English, Japanese, Brazilian Portuguese, and Korean. The registration fee is $200 USD (local taxes may apply).

The passing score is 70%. Databricks notes that exams may include additional unscored items used to gather statistical data for future exam development; these items are not identified and do not affect the final score, meaning the total number of questions delivered may be slightly higher than the 45 scored items. The certification remains valid for two years, after which candidates must retake the current exam version to recertify.

Professionals holding this certification are positioned for roles at the intersection of software engineering and applied AI, including Generative AI Engineer, LLM Engineer, AI Solution Architect, and MLOps Engineer. Generative AI engineering roles command some of the highest compensation in the technology sector, with average salaries reported around $214,000 annually in the United States; the certification directly signals enterprise-grade deployment skills that go beyond prototyping or research experience.

The generative AI applications market is projected to grow at a CAGR exceeding 46% through 2030, and employer demand for engineers who can bridge the gap between experimental LLM work and production-ready Databricks deployments continues to outpace supply. As Databricks is widely adopted across Fortune 500 companies for data and AI workloads, this certification carries strong recognition among employers already invested in the Databricks ecosystem. It complements other Databricks credentials (such as the Data Engineer Associate or ML Professional certifications) for practitioners building a comprehensive Databricks certification portfolio.