Databricks Certified Generative AI Engineer Associate
Intermediate Level
Use Databricks to build performant generative AI solutions
The Databricks Certified Generative AI Engineer Associate certification exam assesses an individual’s ability to design and implement LLM-enabled solutions using Databricks.
This includes problem decomposition to break down complex requirements into manageable tasks as well as choosing appropriate models, tools and approaches from the current generative AI landscape for developing comprehensive solutions.
It also assesses Databricks-specific tools such as Vector Search for semantic similarity searches, Model Serving for deploying models and solutions, MLflow for managing a solution lifecycle, and Unity Catalog for data governance.
A practical Databricks training that equips engineers and data practitioners to build, govern, deploy, and monitor real-world generative AI applications on the Databricks platform.
This 16-hour intermediate course focuses on developing generative AI solutions with Databricks, including retrieval-based agents, single-agent applications, and comprehensive lifecycle practices.
It covers skills in vector search, document processing, embedding generation, RAG workflows, and tools like MLflow and Unity Catalog.
Learners also gain hands-on experience in evaluating, governing, deploying, and monitoring generative AI systems, with emphasis on performance, cost, and operational best practices for production-ready workflows.
DTB-GAIE: Generative AI Engineering with Databricks
Category
Associate
Exam Duration:
90 minutes
Exam Format:
65 questions
Cost:
200 USD
Language Offered:
English, Japanese, Korean
Recertification:
Every two years to maintain your certified status. To recertify, you must take the current version of the exam
Target Audience:
Data scientists, Machine learning engineers, Data practitioners
Delivery:
Remote proctored or onsite testing center
Validity:
2 years
What are the key skills measured:
- Design Applications
- Data Preparation
- Application Development
- Assembling and Deploying Apps
- Governance
- Evaluation and Monitoring
Who is this for?
- Data Scientists
- Machine Learning Engineers
- Data Practitioners
Validate your data and AI skills on Databricks by earning a Databricks credential.
94% of leaders face AI-critical skill shortages today, with one in three reporting gaps of 40% or more
World Economic Forum – Jobs and Future of Work
46% of organizations currently integrate workforce planning into their AI roadmaps.
World Economic Forum – Jobs and Future of Work
55% of leaders expect significant cost and productivity improvements from agentic AI.
World Economic Forum – Jobs and Future of Work
Validate GenAI Engineering Skills
Demonstrate your ability to design and implement LLM-enabled solutions using Databricks, including selecting appropriate models, tools and approaches for real-world generative AI requirements.
Build Production AI Applications
Develop skills across RAG applications, AI agents, LLM chains, Vector Search and Model Serving. The certification validates capabilities that extend from application design and data preparation through deployment and monitoring.
Strengthen AI Governance Expertise
Build practical knowledge of managing GenAI applications with MLflow and Unity Catalog, including evaluation, monitoring, lifecycle management, access control and governance. These skills become increasingly important as organisations move AI applications into production.
Prepare for Emerging GenAI Roles
Build credentials relevant to professionals moving into Generative AI Engineer, AI Engineer, LLM Engineer and related application development roles where RAG, agents, enterprise data and production AI systems are becoming core responsibilities.
Why choose Trainocate?
Train with Trainocate, an official Databricks Authorized Training Partner with 30+ years of technology training expertise, a global footprint spanning 24 countries and expert-led training designed to build practical Data & AI capabilities.
Ready to build your AI credentials with AWS?
Complete the form below to register your interest.
Frequently Asked Questions (FAQs)
1. Is Databricks Certified Generative AI Engineer Associate worth getting for a generative AI career?
Yes, particularly if you want to build production generative AI applications using enterprise data. The certification assesses practical capabilities across application design, data preparation, development, deployment, governance, evaluation and monitoring.
Its value is strongest for professionals who want to move beyond prompt engineering and demonstrate broader engineering capabilities across the GenAI application lifecycle.
2. What jobs is Databricks Certified Generative AI Engineer Associate relevant to?
The certification is particularly relevant to Generative AI Engineer, AI Engineer, LLM Engineer and other technical roles responsible for building enterprise AI applications.
It can also complement the skills of machine learning engineers, data scientists, data engineers and software engineers whose responsibilities are expanding into RAG, AI agents and LLM-enabled applications.
The certification validates technical capability rather than guaranteeing eligibility for a particular job.
3. What skills does the Databricks Generative AI Engineer certification validate?
It validates the ability to design, develop, deploy, evaluate and govern generative AI applications using Databricks.
The current exam includes prompt and model selection, data preparation, RAG, Vector Search, AI agents, MLflow, Model Serving, Unity Catalog, application governance, evaluation and monitoring.
This gives the certification considerably broader career relevance than credentials focused primarily on generative AI concepts.
4. Is this certification useful for data engineers and machine learning engineers?
Yes. Data engineers and machine learning engineers already possess many of the technical foundations needed to transition into generative AI engineering.
Data engineers can extend their skills into RAG pipelines, vector search and AI-ready data architectures, while ML engineers can expand into LLM applications, AI agents, model serving, evaluation and GenAI operations.
The current exam also expects working knowledge of Python, APIs and libraries used for RAG applications, agents and LLM chain development.
5. Does this certification cover AI agents as well as generative AI?
Yes. The current Databricks Certified Generative AI Engineer Associate exam explicitly includes AI agent development.
The March 2026 exam guide covers defining and ordering tools for multi-stage reasoning and determining when to use Agent Bricks capabilities such as Knowledge Assistant, Multiagent Supervisor and Information Extraction. Databricks’ current platform documentation also supports building everything from simple LLM applications to tool-calling and multi-agent systems.
6. Do I need experience before taking the Databricks Certified Generative AI Engineer Associate exam?
There is no formal prerequisite, but Databricks highly recommends related training and six months of hands-on experience.
Databricks recommends its current Generative AI Engineering learning pathway, alongside knowledge of LLMs, prompt engineering, Python, RAG applications, agents, LLM chains and relevant APIs.
For professionals seeking career value rather than simply passing the exam, hands-on experience building GenAI applications should complement certification preparation.
