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Catalog / All levels / Databricks Certified Generative AI Engineer Associate Master Series with 10 Practice Exams
Mammoth Club All levels 19 sections 115 lectures

Databricks Certified Generative AI Engineer Associate Master Series with 10 Practice Exams

What if you could go from curious to certified—and build real LLM-powered apps along the way? This bundle shows you how.

MC
Created by Team Mammoth
01
Skill level
All levels
02
Sections
19
03
Lectures
115
04
Instructor
Team Mammoth
What's inside

This course includes.

✓
19
Sections
✓
115
Lectures
✓
1
Resources
✓
71
Quizzes
✓
Certificate of completion
Included
✓
Mobile and desktop access
Included
✓
AI learning assistance
Included
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Course content

Curriculum & lectures.

9 sections · 61 lectures
+ Welcome 3 lectures
Why should YOU get certified? Locked
What You'll Learn Locked
What You'll Need Locked
+ Fundamentals 2 lectures
Fundamentals of Databricks Generative AI Locked
Submit a Question / Feedback Locked
+ Design Applications 5 lectures
Design a prompt that elicits a specifically formatted response Locked
Select model tasks to accomplish a given business requirement Locked
Select chain components for a desired model input and output Locked
Translate business use case goals into desired inputs and outputs for the AI pipeline Locked
Define and order tools that gather knowledge or take actions for multi-stage reasoning Locked
+ Data Preparation 9 lectures
Apply a chunking strategy for a given document structure and model constraints Locked
Filter extraneous content in source documents that degrades quality of a RAG application Locked
Choose the appropriate Python package to extract document content from source data and format Locked
Define operations and sequence to write given chunked text into Delta Lake tables in Unity Catalog Locked
Identify needed source documents that provide necessary knowledge and quality for a RAG application Locked
Identify prompt/response pairs that align with a given model task Locked
Use tools and metrics to evaluate retrieval performance Locked
Design retrieval systems using advanced chunking strategies. Locked
Explain the role of re-ranking in the information retrieval process. Locked
+ Application Development 16 lectures
Create tools needed to extract data for a given data retrieval need Locked
Select Langchain and similar tools for use in a Generative AI application. Locked
Identify how prompt formats can change model outputs and results Locked
Qualitatively assess responses to identify common issues such as quality and safety Locked
Select chunking strategy based on model & retrieval evaluation Locked
Augment a prompt with additional context from a user's input based on key fields, terms, and intents Locked
Create a prompt that adjusts an LLM's response from a baseline to a desired output Locked
Implement LLM guardrails to prevent negative outcomes Locked
Write metaprompts that minimize hallucinations or leaking private data Locked
Build agent prompt templates exposing available functions Locked
Select the best LLM based on the attributes of the application to be developed Locked
Select embedding model context length based on documents, queries and optimization Locked
Select a model from hub or marketplace for a task based on metadata or cards Locked
Select the best model for a given task based on common metrics generated in experiments Locked
Create a prompt that adjusts an LLM's response from a baseline to a desired output Locked
Utilize Agent Framework for developing agentic systems Locked
+ Assembling and Deploying Applications 12 lectures
Code a chain using a pyfunc model with pre- and post-processing Locked
Control access to resources from model serving endpoints Locked
Code a simple chain according to requirements Locked
Code a simple chain using langchain Locked
Choose RAG app elements: flavor, embedding model, retriever, dependencies, examples, signature Locked
Register the model to Unity Catalog using MLflow Locked
Sequence the steps needed to deploy an endpoint for a basic RAG application Locked
Create and query a Vector Search index Locked
Identify how to serve an LLM application that leverages Foundation Model APIs Locked
Identify resources needed to serve features for a RAG application Locked
Explain the key concepts and components of Mosaic AI Vector Search Locked
Identify batch inference workloads and apply ai_query() appropriately Locked
+ Governance 5 lectures
Use masking techniques as guard rails to meet a performance objective Locked
Select guardrail techniques to protect against malicious user inputs to a Gen AI application Locked
Recommend an alternative for problematic text mitigation in a data source feeding a RAG application Locked
Use legal and licensing requirements for data sources to avoid legal risk Locked
Recommend alternatives for problematic text mitigation in a data source feeding a GenAI application Locked
+ Evaluation and Monitoring 8 lectures
Select an LLM choice (size and architecture) based on a set of quantitative evaluation metrics Locked
Select key metrics to monitor for a specific LLM deployment scenario Locked
Evaluate model performance in a RAG application using MLflow Locked
Use inference logging to assess deployed RAG application performance Locked
Use Databricks features to control LLM costs for RAG applications Locked
Use inference tables and Agent Monitoring to track a live LLM endpoint Locked
Identify evaluation judges that require ground truth Locked
Compare the evaluation and monitoring phases of the Gen AI application life cycle Locked
+ Challenge Your 10 FREE Practice Exams 1 lecture
Where to go from here Locked
Description

About this course.

Get fully prepared for the Databricks Certified Generative AI Engineer Associate exam with hands-on, focused training that turns theory into results.


✅ Break down complex requirements using proven problem decomposition strategies

✅ Choose the right models, tools, and frameworks from today’s generative AI ecosystem

✅ Master Databricks-specific tools like Vector Search, Model Serving, Unity Catalog, and MLflow

✅ Learn to design, build, and deploy real-world RAG (Retrieval-Augmented Generation) apps and LLM chains

✅ Build a solid foundation in data governance, solution lifecycle management, and semantic search

📝 Get exam-ready with 10 practice exams and unlimited attempts


Boost your learning with 🧠 critical thinking challenges, 🧩 logic puzzles, ✍️ mnemonics, 📚 interactive quizzes, 📖 real-world stories, 📌 expert sidenotes, 📊 case studies, 🆕 latest trends, and 🎁 surprise bonus content!


Whether you’re aiming to pass the exam or prove your skills in a real-world AI role, this bundle gives you the practical experience and exam prep you need to succeed.


🎁 Comes with lifetime access and downloadable study resources. Learn fast. Build smart. Get certified.


If you're ready to become a certified Generative AI Engineer with Databricks—this is your path forward.

Ready to start building?

What if you could go from curious to certified—and build real LLM-powered apps along the way? This bundle shows you how.

Buy lifetime access →