Databricks Certified Generative AI Engineer Associate¶

Exam Overview¶

The Databricks Certified Generative AI Engineer Associate certification validates the ability to design and implement generative AI solutions on the Databricks platform, with a heavy focus on Retrieval-Augmented Generation (RAG) applications. This certification covers Vector Search, Foundation Model APIs, prompt engineering, evaluation frameworks, and governance for GenAI applications.

Exam Details: - Exam Code: Databricks Certified Generative AI Engineer Associate - Duration: 90 minutes - Number of Questions: 45 multiple-choice questions - Passing Score: 70% (approximately 32 correct answers) - Cost: $200 USD - Delivery: Online proctored - Validity: 2 years - Prerequisites: None (familiarity with LLMs and Databricks recommended)

Exam Domains¶

Domain 1: RAG Application Design (30%)¶

• Design RAG application architectures
• Select appropriate chunking and embedding strategies
• Design retrieval pipelines
• Understand RAG tradeoffs and patterns

Key Concepts: - RAG architecture components (retriever, generator, knowledge base) - Document chunking strategies (fixed-size, semantic, recursive) - Chunk size and overlap tradeoffs - Embedding model selection and considerations - Vector database design and indexing - Hybrid search (dense + sparse retrieval) - Re-ranking strategies - Multi-step RAG and agentic RAG patterns - Context window management - RAG vs fine-tuning decision criteria

Domain 2: RAG Application Implementation (30%)¶

• Implement RAG pipelines on Databricks
• Use Vector Search for retrieval
• Integrate Foundation Model APIs
• Build end-to-end RAG applications

Key Concepts: - Databricks Vector Search setup and configuration - Delta Sync Index vs Direct Vector Access Index - Embedding computation and storage - Foundation Model APIs (pay-per-token and provisioned throughput) - External model endpoints (OpenAI, Anthropic, etc.) - Mosaic AI Agent Framework - LangChain integration on Databricks - Prompt engineering for RAG (system prompts, few-shot) - Chain deployment and serving - MLflow for GenAI (tracing, logging)

Domain 3: Governance and Evaluation (20%)¶

• Evaluate RAG application quality
• Implement governance for GenAI
• Monitor GenAI applications
• Handle safety and compliance

Key Concepts: - RAG evaluation metrics (relevance, faithfulness, answer quality) - Mosaic AI Agent Evaluation framework - LLM-as-judge evaluation patterns - Human evaluation workflows - Data governance for GenAI (PII, sensitive data) - Unity Catalog for model and data governance - Guardrails and safety filters - Cost management for LLM usage - Inference table logging - A/B testing for GenAI applications

Domain 4: LLM Fundamentals (20%)¶

• Understand large language model concepts
• Select appropriate models for use cases
• Understand prompt engineering principles
• Know LLM limitations and mitigation strategies

Key Concepts: - Transformer architecture fundamentals - Token-based processing and context windows - Temperature, top-p, and generation parameters - Prompt engineering techniques (zero-shot, few-shot, chain-of-thought) - Foundation models available on Databricks (DBRX, Llama, etc.) - Model selection criteria (size, latency, cost, quality) - Hallucination causes and mitigation - Fine-tuning vs RAG vs prompt engineering - Instruction tuning and RLHF concepts - Multi-modal model awareness

Key Concepts to Master¶

RAG Architecture¶

• Document processing pipeline (ingest, chunk, embed, index)
• Retrieval strategies (similarity search, MMR, filtered search)
• Generation with retrieved context
• End-to-end latency optimization
• Error handling and fallback strategies

Databricks GenAI Stack¶

• Vector Search (managed service on Databricks)
• Foundation Model APIs (serving layer)
• Mosaic AI Agent Framework (building agents)
• Mosaic AI Agent Evaluation (quality assessment)
• MLflow Tracing (observability)
• Unity Catalog (governance)

Evaluation and Quality¶

• Component-level evaluation (retriever, generator)
• End-to-end evaluation (answer quality)
• Automated evaluation with LLMs
• Evaluation datasets and ground truth
• Continuous monitoring of GenAI applications

Study Approach¶

Phase 1: Foundation (Week 1-2)¶

1. Review LLM fundamentals and transformer concepts
2. Understand RAG architecture and design patterns
3. Learn Databricks Vector Search capabilities
4. Study Foundation Model APIs and available models

Phase 2: Implementation (Week 3-4)¶

1. Build a RAG application on Databricks
2. Practice Vector Search indexing and querying
3. Learn Mosaic AI Agent Framework
4. Study evaluation and governance patterns

Phase 3: Exam Prep (Week 5-6)¶

1. Take practice exams and review incorrect answers
2. Focus on RAG design and implementation (60% of exam)
3. Review evaluation frameworks and governance
4. Practice scenario-based questions

Study Resources¶

• Databricks Academy - GenAI learning paths
• Exam Guide - Official exam page
• Vector Search Documentation - Vector Search guide
• Foundation Model APIs - Model serving docs
• Mosaic AI Documentation - Agent framework guide
• RAG Tutorial - Hands-on RAG guide

Tips for Success¶

1. RAG dominates the exam - 60% covers RAG design and implementation
2. Know Vector Search deeply - Delta Sync Index vs Direct Vector Access Index
3. Chunking strategies matter - Understand tradeoffs of different approaches
4. Evaluation is critical - Know how to assess RAG quality systematically
5. Foundation Model APIs - Know pay-per-token vs provisioned throughput
6. Governance for GenAI - PII handling, safety filters, and compliance
7. Prompt engineering - Practical techniques, not just theory
8. Mosaic AI tools - Agent Framework and Agent Evaluation
9. Hands-on practice - Build at least one RAG application on Databricks
10. LLM limitations - Know hallucination causes and mitigation strategies

File Index¶