Databricks Certified Generative AI Engineer Associate: Visual Architecture Field Guide

Generative AI systems are no longer prototypes—they are production platforms handling enterprise data, customer interactions, and business-critical workflows. Modern certification exams for Generative AI Engineers and Architects increasingly test whether you understand end-to-end system design, not just isolated tools or APIs.

This visual field guide breaks down GenAI architecture patterns using sketchnotes that compress complex systems into memorable mental models. Instead of memorizing definitions, you’ll learn how architectural decisions are made, why certain patterns dominate in enterprise environments, and how these concepts appear in scenario-based exam questions.

Whether you are preparing for a Generative AI Engineer Associate exam, designing production-grade GenAI systems, or refreshing architecture fundamentals, this guide provides a clear, exam-aligned roadmap.

What This Visual Guide Covers

• Core GenAI architectural patterns (Pre-training, Fine-tuning, RAG)

• RAG data pipelines and vectorization workflows

• Vector search and hybrid retrieval strategies

• Prompt engineering structure and reasoning patterns

• Model serving, throughput, and cost optimization

• LLM evaluation using LLM-as-a-Judge

• Governance, security, and access control

• Observability, tracing, and LLMOps lifecycle management

Architectural Patterns for Generative AI Systems

(Pre-training vs Fine-tuning vs RAG)

At a high level, GenAI systems are built using one of three architectural strategies:

Pre-training

Pre-training involves training a foundation model from scratch using massive datasets over extended periods.

• Extremely high cost

• Long training timelines

• Rarely feasible outside large AI vendors

Exam relevance:
Pre-training is usually a distractor option in exams. Enterprise teams almost never choose this approach.

Fine-tuning

Fine-tuning adapts a base model using task-specific datasets.

• Improves domain specificity

• Requires labeled data

• Introduces model versioning complexity

Exam relevance:
Fine-tuning is appropriate when behavioral changes are required, not when knowledge updates are frequent.

Retrieval-Augmented Generation (RAG)

RAG retrieves external knowledge at inference time and injects it into prompts.

• No retraining required

• Dynamic, up-to-date knowledge

• Lower cost and faster iteration

Exam Tip:
Most enterprise and certification scenarios favor RAG over fine-tuning because it separates knowledge from model behavior.

RAG Data Pipelines: From Documents to Vectors

A RAG pipeline converts unstructured documents into retrievable vector representations.

Key Stages

1. Parsing – Extract text from PDFs, HTML, and structured sources

2. Chunking – Split content into retrievable units

3. Embedding – Convert text into vector representations

4. Storage – Persist vectors in a vector database or index

Chunking Strategy Matters

• Fixed-size chunks are fast but may lose meaning

• Semantic or paragraph-based chunks preserve context

Exam Tip:
Chunking strategy often has more impact on retrieval quality than the embedding model itself.

Vector Search and Hybrid Retrieval

Vector search finds semantically similar content using approximate nearest neighbor (ANN) algorithms such as HNSW.

Hybrid Retrieval

Enterprise systems often combine:

• Keyword search (BM25)

• Vector similarity search

Using techniques like Reciprocal Rank Fusion, hybrid retrieval balances precision and recall.

Exam Insight:
Hybrid search is preferred when queries include both exact terms and semantic intent.

Application Logic: AI Functions and Frameworks

GenAI application logic sits between data and model intelligence.

Common Approaches

• SQL-based AI functions for simplicity

• Agent frameworks for orchestration

• Pre-wired task-specific functions for speed

Exam Focus:
Questions test where logic belongs—data layer, orchestration layer, or model layer.

Prompt Engineering Anatomy (High-Yield Topic)

Well-structured prompts contain four components:

1. Instruction – What the model should do

2. Context – Retrieved documents or system state

3. Input Data – User-specific content

4. Output Indicator – Format or constraints

Reasoning Enhancers

• Few-shot examples

• Chain-of-Thought prompting

• Prompt templates for consistency

Exam Tip:
Exams test prompt structure decisions, not creative wording.

Model Serving, Throughput, and Cost Optimization

Production GenAI systems must balance performance and cost.

Serving Options

• Pay-per-token – Flexible, ideal for spiky traffic

• Provisioned throughput – Predictable, optimized for steady workloads

Key Metric

• Tokens per second (input + output)

Exam Insight:
Generating output tokens is slower and more expensive than reading input tokens.

Evaluation Using LLM-as-a-Judge

Traditional evaluation metrics struggle with open-ended GenAI outputs.

LLM-as-a-Judge Evaluates:

• Correctness

• Safety (toxicity, PII)

• Hallucination and faithfulness

Human feedback loops validate and calibrate automated judgment.

Exam Focus:
Evaluation pipelines are essential for continuous improvement, not just testing.

Governance and Security in GenAI Systems

Enterprise GenAI systems require centralized governance.

Governed Assets

• Models

• Data

• Functions

• Files

Key Capabilities

• Role-based access control (RBAC)

• Lineage and auditability

• Unified policy enforcement

Exam Insight:
Models are governed like data assets, not standalone artifacts.

Observability and Tracing Across the Pipeline

End-to-end tracing reveals where latency, cost, and errors occur.

Traced Stages

• User query

• Retrieval

• Prompt assembly

• Model generation

Exam Tip:
Observability is a production requirement, not an optional enhancement.

LLMOps Lifecycle Management

GenAI systems follow a continuous lifecycle:

• Experimentation

• Prompt engineering

• Evaluation

• Deployment

• Monitoring and feedback

Core Principle

Deploy code, not just models.

Version prompts, pipelines, and logic alongside model weights.

The GenAI Architect’s Checklist

Before declaring a GenAI system production-ready, ensure:

• Architecture pattern selected (RAG or fine-tuning)

• Data properly chunked and indexed

• Governance applied consistently

• Evaluation automated

• Serving optimized for workload

How to Use This Guide for Exam Preparation

• First pass: Build system-level understanding

• Second pass: Map visuals to scenario questions

• Final revision: Use visuals as rapid recall anchors

For best results, pair these visuals with domain-based practice questions and exam simulations on FlashGenius.

👉 Practice GenAI architecture and RAG scenario questions on FlashGenius

Final Thoughts

Modern GenAI certifications reward engineers who understand systems, tradeoffs, and production realities. This visual field guide turns complex architectures into clear mental models you can apply both in exams and real-world systems.

Use it not to memorize—but to think like a GenAI architect.