Databricks Generative AI Engineer Certification

Module 1: GenAI on Databricks Overview

• Databricks Generative AI Engineer exam: format, domains, and target audience
• GenAI landscape: foundation models, fine-tuning, and RAG -- when to use which
• Databricks AI tooling: Model Serving, Vector Search, AI Playground, and DBRX
• Indian GenAI use cases: legal document summarisation, regional language Q&A, and more

Module 2: Large Language Model Fundamentals

• Transformer architecture: attention mechanisms and tokenisation for engineers
• Open-source vs proprietary LLMs: trade-offs for Indian enterprise deployments
• Prompt engineering: system prompts, few-shot examples, and chain-of-thought
• LLM evaluation metrics: BLEU, ROUGE, and human preference scoring explained

Module 3: Retrieval-Augmented Generation (RAG)

• RAG architecture: retriever + generator design for knowledge-grounded responses
• Databricks Vector Search: creating and querying vector indexes on Delta tables
• Embedding models: choosing the right model for Hindi and English multilingual RAG
• RAG evaluation: context precision, recall, and faithfulness metrics for exam

Module 4: Building GenAI Applications

• LangChain on Databricks: chains, agents, and memory for production applications
• LlamaIndex integration: document indexing and retrieval pipeline construction
• AI Playground: rapid prototyping and model comparison for application developers
• Capstone mini-project: building a GST regulation Q&A chatbot on Databricks

Module 5: MLflow for GenAI

• MLflow LLM tracking: logging prompts, responses, and evaluation metrics
• MLflow AI Gateway: managing multiple LLM providers with unified API interface
• Prompt versioning: tracking prompt changes alongside model versions in registry
• Practice: 20 MLflow GenAI questions aligned to certification exam domains

Module 6: Fine-Tuning & Model Customisation

• When to fine-tune: domain adaptation for Indian legal, medical, and financial text
• LoRA and QLoRA: efficient fine-tuning techniques on Databricks GPU clusters
• Instruction tuning: creating supervised fine-tuning datasets from Indian sources
• Evaluation and safety: testing fine-tuned models for hallucination and bias

Module 7: Production GenAI & Responsible AI

• Serving LLMs: Databricks Model Serving for real-time GenAI API endpoints
• Guardrails: implementing content filtering and toxicity detection in pipelines
• Cost management: token budgeting and caching strategies for high-traffic apps
• Responsible AI in India: bias, transparency, and DPDP Act implications for GenAI

Module 8: Mock Exam & Certification Readiness

• Full 45-question timed mock at Generative AI Engineer certification difficulty
• Answer review: covering RAG, MLflow, fine-tuning, and responsible AI domains
• Exam-day reference card: key concepts, APIs, and architectural patterns
• Career guide: GenAI Engineer roles, salaries, and growth outlook in Indian tech

Experience in the Industry Learn from Databricks-certified data engineers and ML engineers who have built production lakehouse platforms, Delta Live Tables pipelines, and ML workflows on Databricks for data-driven enterprises.

Backgrounds at the Top Our Databricks trainers have delivered data platform projects at technology companies, financial services firms, and healthcare organisations where Databricks is the central platform for data engineering and machine learning.

Clear & Effective Teaching Databricks architecture, Spark DataFrames, Delta Lake, Databricks SQL, MLflow, Delta Live Tables, Unity Catalog, and governance are explained clearly with real lakehouse data platform examples.

Hands-On Learning Focus Students build end-to-end lakehouse pipelines, work with Delta tables, create DLT pipelines, track ML experiments with MLflow, and configure Unity Catalog governance through structured lab exercises.

Up-to-Date Knowledge Trainers keep content current with the latest Databricks platform releases, Databricks AI and GenAI capabilities, Unity Catalog enhancements, and evolving lakehouse architecture best practices.