Databricks Generative AI Engineer Associate Exam Prep (2026)

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

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

Language: English | Duration: 3h 52m | Size: 1.62 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.