Databricks Generative AI Engineer Associate Exam Prep

Databricks Generative AI Engineer Associate Exam Prep
https://WebToolTip.com
Published 10/2026

Created by Edcorner Learning -190000+ Students, 4+ Star Ratings

MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch

Level: All Levels | Genre: eLearning | Language: English | Duration: 30 Lectures ( 7h 59m ) | Size: 1.7 GB
Think like the exam with 100 Databricks GenAI scenarios, RAG, Vector Search, MLflow, Model Serving and Unity Catalog
What you'll learn

⚡ Prepare for the Databricks Certified Generative AI Engineer Associate exam using a practical, scenario-based learning approach.

⚡ Learn how to think like the exam by identifying scenario clues, eliminating distractors, and choosing the best Databricks-native solution.

⚡ Understand key Generative AI engineering concepts including RAG, LLMs, embeddings, Vector Search, chunking, prompt engineering, agents, governance, evaluation,

⚡ Design Retrieval-Augmented Generation applications using document chunking, embeddings, metadata filters, Vector Search, and grounded responses.

⚡ Recognize when to use RAG, Feature Serving, SQL tools, fine-tuning, agent workflows, or prompt engineering based on the business requirement.

⚡ Understand how Databricks Vector Search supports semantic retrieval for manuals, policies, support logs, product data, legal documents, and enterprise knowledge

⚡ Learn how to prepare GenAI data using Delta tables, chunk-level records, unique chunk IDs, metadata, OCR, and clean source documents.

⚡ Understand how MLflow supports model tracking, model logging, signatures, input examples, artifacts, and model lifecycle management.

⚡ Learn when to use MLflow PyFunc for custom preprocessing, prompt construction, retrieval logic, post-processing, and structured inference workflows.

⚡ Understand how Databricks Model Serving supports production deployment of LLM applications and model endpoints.

⚡ Learn production deployment concepts such as secure credentials, Databricks Secrets, Foundation Model APIs, Provisioned Throughput, latency, throughput, and tok

⚡ Understand how Unity Catalog supports governance, access control, lineage, model registration, secure sharing, and cross-workspace access.

⚡ Learn how to apply guardrails for safety, security, compliance, topic boundaries, harmful content, prompt injection, and off-domain requests.

⚡ Understand agentic GenAI patterns including tool calling, ReAct-style reasoning, LangChain-style workflows, LangGraph-style orchestration, and multi-step LLM pi

⚡ Learn how to evaluate GenAI and RAG systems using precision, recall, Recall@k, NDCG, BLEU, ROUGE, cosine similarity, latency, throughput, and token usage.

⚡ Practice 100 exam-style Databricks GenAI scenarios covering real-world use cases in customer support, healthcare, finance, legal, product search, code generatio

⚡ - Use module study guides and a final cheat sheet to revise key exam concepts, memory rules, tool-selection rules, and scenario decision patterns.
Requirements

❗ Learners should be willing to study through scenario-based explanations, not just memorize definitions.