Recently I earned the Databricks Machine Learning Professional certification and wanted to share my study journey. Before the exam, I worked on a project as a data engineer alongside data scientists (ML models, LLMs, MLflow). That led me to build a personal RAG project on Databricks, which ended up preparing me for many exam topics. Below is a compact flow of that project plus the official prep guide and a Udemy practice test. 

My Databricks RAG Lab - flow summary 

1) Goal & stack 

- Goal: practice end-to-end (ingest -> embed -> retrieve -> answer) on Databricks 

- Stack: Databricks Runtime; PyMuPDF (PDF parsing); Hugging Face E5 via Databricks Model Serving; PostgreSQL + pgvector (vector store); LangChain (RAG); secrets with dbutils.secrets; Jobs/Workflows (orchestration) 

2) Ingestion 

- Upload PDFs to DBFS (/tmp/.../docs/) with UUID filenames 

- Keep upload separated from processing for scale and observability 

3) Orchestration 

- Databricks Workflow scans the folder and triggers the processor notebook with the file path as a parameter 

- Simple, re-runnable pipeline 

4) Parsing & chunking 

- Extract text with PyMuPDF 

- Chunk to fit embedding/token limits and carry metadata (file, user, timestamps) 

5) Embeddings (Model Serving) 

- Call a Model Serving endpoint with E5 to generate embeddings 

- Decouple model choice so you can swap models without rewriting the pipeline 

6) Vector storage 

- Store chunks + vectors + metadata in PostgreSQL/pgvector 

- Use SQL for Top-K similarity; easy to debug and cost-predictable 

7) Retrieval (Top-K) 

- Embed the question -> run Top-K vector search in pgvector -> fetch relevant chunks 

😎 Generation (RAG) 

- Build a prompt with question + retrieved chunks and call an LLM endpoint for grounded answers 

9) Ops, security, observability 

- Secrets via secret scopes (DB creds, endpoints) 

- Layout ready for multi-tenant isolation if needed 

- Simple metrics (latency, Top-K size, document counts); optional MLflow versioning 

10) Why this helped for the exam 

- Exercises Jobs/Workflows and Model Serving (orchestration and deployment) 

- Hands-on with feature engineering/embeddings and modular pipelines 

- MLOps basics: reproducibility, secrets, cost/performance trade-offs 

- Practice discussing governance and best practices aligned to exam objectives 

Official prep guide: 
https://www.databricks.com/learn/certification/machine-learning-professional 
Udemy practice test: 
https://www.udemy.com/course/databricks-machine-learning-professional-practice-test/ 

I wish you all the best!