Databricks Certified: Generative Ai Engineer Associate
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Databricks Certified: Generative Ai Engineer Associate
Published 1/2026
Created by Kuljot Singh Bakshi
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 40 Lectures ( 7h 12m ) | Size: 5.84 GB[/center]
Master RAG, Vector Search, Embeddings, MLflow, and GenAI workflows to pass the Databricks GenAI Engineer exam What you'll learn
✓ Understand the Databricks Generative AI architecture, including foundation models, embeddings, vector search, and Mosaic AI tooling.
✓ Build and deploy Retrieval-Augmented Generation (RAG) applications using Databricks, Vector Search, and MLflow.
✓ Apply prompt engineering techniques to improve LLM accuracy, grounding, and reliability in Databricks workloads.
✓ Use Databricks Vector Search and embeddings effectively for semantic search, document retrieval, and AI applications.
✓ Evaluate, monitor, and version GenAI models using MLflow, experiments, and production best practices.
✓ Implement responsible and secure GenAI solutions following Databricks governance, access control, and cost-aware design.
✓ Prepare confidently for the Databricks Certified GenAI Engineer Associate exam with exam-focused explanations and real-world examples. Requirements
● Basic understanding of Python programming (loops, functions, and working with notebooks).
● A Databricks Managed version by a Cloud Service Provider (CSP)
● A Databricks account or workspace access (Community Edition or enterprise) for hands-on practice.
● Interest in Generative AI and LLM-based applications - no prior GenAI experience is required. Description
This course is a complete, exam-aligned guide to the Databricks Certified Generative AI Engineer Associate certification, designed for professionals who want to build, deploy, and manage Generative AI applications on Databricks with confidence.
Generative AI on Databricks goes far beyond prompt writing. To succeed in real-world projects-and in the certification exam-you must understand how foundation models, embeddings, vector search, RAG pipelines, MLflow, and governance work together. This course focuses exactly on those skills.
You will learn how to design and implement Retrieval-Augmented Generation (RAG) systems, use Databricks Vector Search for semantic retrieval, manage embeddings effectively, and integrate LLMs into scalable data and analytics workflows. Every concept is explained with a clear mental model, followed by hands-on demonstrations using Databricks-native tools.
The course is structured to closely align with the official Databricks exam blueprint, helping you understand not just what to do, but why it works-an essential skill for both certification success and real-world engineering.
In this course, you will
• Understand Databricks' Generative AI architecture and ecosystem
• Build end-to-end RAG applications using embeddings and Vector Search
• Apply prompt engineering techniques for reliable and grounded outputs
• Track, evaluate, and manage GenAI models using MLflow
• Follow best practices for governance, security, and cost awareness
• Prepare confidently for the Databricks Certified GenAI Engineer Associate exam
Whether you are preparing for certification or looking to upskill in production-grade Generative AI on Databricks, this course provides a structured, practical, and exam-focused learning path. Who this course is for
■ Data engineers, analytics engineers, and machine learning practitioners who want to build GenAI applications on Databricks.
■ Professionals preparing for the Databricks Certified Generative AI Engineer Associate exam and looking for a structured, exam-aligned course.
■ Data scientists and ML engineers who want hands-on experience with RAG, embeddings, and Vector Search using Databricks.
■ Software engineers and platform engineers interested in integrating LLMs into data and analytics workflows.
■ Databricks users and cloud data professionals looking to upskill in Generative AI using Databricks-native tools.
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Databricks Certified: Generative Ai Engineer Associate
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