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Databricks Certified Generative AI Engineer Certification Course

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84 expert modules derived from 60+ exam-style questions. Covers every domain and scenario : organized by blueprint weight so you study what matters most.

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Databricks Certified Generative AI Engineer

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About This Course

Databricks Certified Generative AI Engineer · 84 modules

This course covers every domain tested on the Databricks Certified Generative AI Engineer exam. Based on our 60+ real practice questions and prepared by certification experts.

info What you'll learn:

Every exam domain with detailed explanations
Common exam traps that catch unprepared candidates
Key concepts, syntax, and configurations
Real-world scenarios aligned with exam objectives
Quick-reference cheat sheets for last-minute review

Your Databricks Certified Generative AI Engineer Roadmap

Databricks Certified Generative AI Engineer certification preparation infographic

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The remaining 66 modules cover advanced topics, exam traps, and scenarios that appear on the certification exam.

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Complete Exam-Focused Course and Final Revision Guide

Exam alignment: This course is aligned to the currently live Databricks Certified Generative AI Engineer Associate exam guide dated March 18, 2026.
Purpose: Learn the architecture decisions, Databricks services, tradeoffs, and exam traps that appear repeatedly in realistic scenario questions.
How to use this guide: Read the course once from start to finish, then use the memory rules, service-selection tables, and exam-day checklist for rapid revision.

1. Exam Overview

The Databricks Certified Generative AI Engineer Associate exam tests whether you can design, build, deploy, govern, evaluate, and monitor LLM-enabled solutions on Databricks.

The exam is not mainly a vocabulary test. Most questions give a business scenario and ask you to choose the best architecture, retrieval strategy, deployment pattern, or governance control. Several answers may sound technically possible. Your job is to identify the option that best satisfies the stated constraints.

Current Exam Format

Item	Current Exam Detail
Exam name	Databricks Certified Generative AI Engineer Associate
Scored questions	45
Question styles	Multiple-choice and multiple-selection
Time limit	90 minutes
Delivery	Online proctored
Prerequisites	None required
Recommended experience	Six months of hands-on experience with the tested tasks
Certification validity	Two years
Test aids	None
Code context	Python for ML tasks; SQL may appear for data manipulation or workflow examples

Official Domain Weights

Domain	Weight	Approximate Focus in a 45-Question Exam
1. Design Applications	14%	About 6 questions
2. Data Preparation	14%	About 6 questions
3. Application Development	30%	About 13–14 questions
4. Assembling and Deploying Applications	22%	About 10 questions
5. Governance	8%	About 3–4 questions
6. Evaluation and Monitoring	12%	About 5–6 questions

What the Exam Rewards

A strong candidate consistently chooses solutions that are:

Grounded: answers use authoritative evidence rather than model guesses.
Governed: data, models, prompts, tools, and logs are managed with appropriate access controls.
Measurable: retrieval quality, response quality, latency, throughput, safety, and cost are evaluated explicitly.
Maintainable: versions, aliases, CI/CD gates, traceability, and rollback paths are built into the design.
Efficient: the architecture is no more complex or expensive than the use case requires.
Production-ready: credentials stay out of browsers and prompts; endpoints use least privilege; monitoring continues after deployment.

2. Exam Domains

Domain 1: Design Applications : 14%

You must translate business requirements into the right AI workflow. Expect questions about:

Structured prompt outputs
NLP task selection
RAG chain ordering
Tool ordering for multi-stage reasoning
Agent Bricks selection:
- Knowledge Assistant
- Information Extraction
- Multiagent Supervisor

Domain 2: Data Preparation : 14%

You must prepare source content so retrieval works well. Expect questions about:

Chunk size and overlap
Structure-aware chunking
Cleaning noisy documents
OCR and HTML parsing tools
Delta Lake tables in Unity Catalog
Authoritative source selection
Retrieval precision and recall
Parent-child retrieval
Reranking

Domain 3: Application Development : 30%

This is the highest-weight domain. Expect questions about:

Chains, agents, LangChain, and similar frameworks
Prompt engineering and prompt augmentation
Guardrails and prompt-injection defense
Model selection
Embedding context length
Model cards and marketplace metadata
MLflow tracing
Evaluation versus monitoring
Multi-agent systems using Genie Spaces or conversational APIs

Domain 4: Assembling and Deploying Applications : 22%

This is the second-highest-weight domain. Expect questions about:

pyfunc packaging with preprocessing and post-processing
Model Serving endpoint permissions
RAG application components
Unity Catalog model registration with MLflow
Mosaic AI Vector Search
Foundation Model APIs
ai_query() for batch inference
Persistent memory
CI/CD for agents
Managed, external, and custom MCP servers
MLflow Prompt Registry lifecycle management
User-facing interfaces such as Databricks Apps, Slack, and Teams

Domain 5: Governance : 8%

Expect short but important questions about:

Masking and minimization
Prompt-injection defense
Least privilege
Source licensing
Problematic source-text remediation
Traceability after remediation

Domain 6: Evaluation and Monitoring : 12%

Expect questions about:

Model selection using quantitative metrics
Production metrics
MLflow scorers and traces
Inference tables
Agent Monitoring
AI Gateway
Rate limiting
Custom scorers
Ground-truth judges
SME feedback
Cost control

3. Start-to-Finish Study Path

Use this order because each stage depends on the previous one.

Stage 1: Understand the Workload

Before selecting services, determine:

What question or action must the application support?
Is the required fact static, semi-static, or live?
Is the output free text, a fixed label, a summary, or a structured schema?
Does the workflow require one deterministic sequence or dynamic tool selection?
Which data is sensitive?
Which metrics define success?

Fast Decision Rule

Requirement	Best Starting Point
Answer questions from enterprise documents	RAG or Agent Bricks Knowledge Assistant
Extract structured fields from documents	Agent Bricks Information Extraction
Coordinate specialist agents or tools	Multiagent Supervisor
Summarize a memo	Summarization model task
Assign an approved category	Classification
Retrieve live account-specific status	Structured lookup tool or operational table
Enrich many rows in a table	`ai_query()` batch inference
Support live multi-turn interactions	Served agent or chain endpoint

Stage 2: Prepare Sources

A strong retrieval application begins with clean, governed source data.

Identify authoritative sources.
Separate static knowledge from live operational facts.
Extract text correctly.
Remove noise.
Chunk with structure-aware rules.
Store chunk text and metadata in governed Delta tables.
Create or refresh the retrieval index.
Evaluate precision and recall.
Add reranking only when the relevance gain justifies the latency.

Stage 3: Develop the Application

Choose a chain when steps are fixed.
Choose an agent when tool selection is dynamic.
Define tool descriptions and schemas clearly.
Add prompt instructions, output contracts, and fallbacks.
Apply least privilege and prompt-injection defenses.
Compare models using the same representative dataset.
Trace intermediate steps with MLflow.

Stage 4: Package and Deploy

Package preprocessing, model calls, and post-processing consistently.
Register deployable models in Unity Catalog using MLflow.
Configure Mosaic AI Vector Search for scale, updates, latency, and cost.
Use Prompt Registry versions and aliases.
Promote components through gated environments.
Refresh dependent indexes during releases.
Expose the solution through a secure authenticated backend.

Stage 5: Evaluate and Monitor

Run offline evaluation before release.
Monitor live production traffic after release.
Use inference tables, traces, and scorers.
Track safety, quality, latency, throughput, error rates, and cost.
Add production failures back into the offline evaluation dataset.
Use calibrated SME feedback for domain-specific quality.

4. Core Concepts by Domain

Domain 1 : Design Applications

4.1 Structured Prompt Outputs

When a downstream system needs machine-readable output, do not merely ask the model to “be concise” or “respond in JSON.” Define the contract explicitly.

Strong Prompt Pattern

Include:

Required schema
Field names
Allowed values
Data types
Missing-value behavior
Output-only instruction
One valid example
Validation after generation

Example

Return only valid JSON with:
{
  "category": one of ["billing", "shipping", "returns"],
  "priority": one of ["low", "medium", "high"],
  "reason": string
}
Use "unknown" when the source evidence is insufficient.

Exam Trap

Wrong answer: Increase temperature to improve output variety.
Why it fails: A structured output problem needs tighter constraints and validation, not more variability.

4.2 Choose the Right Model Task

Business Requirement	Best Model Task
Convert a paragraph into a one-sentence gist	Summarization
Assign a support ticket to one approved category	Classification
Generate a natural-language answer	Text generation
Extract named people, organizations, or locations	Named-entity extraction
Produce structured fields from documents	Information extraction

Exam Trap

A question may describe a memo that must become one sentence. The answer is usually summarization, not classification, sentiment analysis, or named-entity recognition.

4.3 Order a RAG Chain Correctly

The standard retrieval-augmented generation flow is:

Receive the user query.
Extract key fields, intent, and authorization context.
Retrieve relevant evidence.
Apply metadata filters.
Optionally rerank candidate passages.
Assemble a grounded prompt.
Call the LLM.
Validate and format the result.
Log traces and evaluation signals.

Memory Rule

Retrieve before generate. Validate after generate.

Exam Trap

Wrong answer: Generate first, then retrieve documents to justify the answer.
Why it fails: Post-hoc retrieval cannot ground the original response.

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4.4 Design Tool-Using Workflows

This module is part of the full course. Unlock all 84 modules + 60+ practice questions.

Databricks Certified Generative AI Engineer Certification Course

About This Course

Your Databricks Certified Generative AI Engineer Roadmap

Complete Exam-Focused Course and Final Revision Guide

1. Exam Overview

Current Exam Format

Official Domain Weights

What the Exam Rewards

2. Exam Domains

Domain 1: Design Applications : 14%

Domain 2: Data Preparation : 14%

Domain 3: Application Development : 30%

Domain 4: Assembling and Deploying Applications : 22%

Domain 5: Governance : 8%

Domain 6: Evaluation and Monitoring : 12%

3. Start-to-Finish Study Path

Stage 1: Understand the Workload

Fast Decision Rule

Stage 2: Prepare Sources

Stage 3: Develop the Application

Stage 4: Package and Deploy

Stage 5: Evaluate and Monitor

4. Core Concepts by Domain

Domain 1 : Design Applications

4.1 Structured Prompt Outputs

Strong Prompt Pattern

Example

Exam Trap

4.2 Choose the Right Model Task

Exam Trap

4.3 Order a RAG Chain Correctly

Memory Rule

Exam Trap

Stop guessing. Start passing.

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