Certification roadmap

Generative AI Engineer Associate

The associate-level certification for designing and implementing LLM-enabled solutions on Databricks, covering prompt design, RAG pipelines, agent frameworks, AI Search (formerly Vector Search), Model Serving, and MLflow-based evaluation.

Exam guide 2026-03 Official page 45 questions90 minutes0 / 34 done

This exam guide does not publish per-domain weights, so the sections below are in the guide's own order and carry no percentage.

Names this guide still uses

8 renamed since 2026-03All renames

The exam still asks in the old words. The product answers in the new ones. Left is what you will read in the question; right is what you will click in the workspace.

In the examMosaic AI GatewayIn the productUnity Gateway2026-08-04 In the examGenie spacesIn the productGenie Agents2026-07 In the examMosaic AI Vector SearchIn the productAI Search2026-06-01 In the examDatabricks Asset Bundles (DABs)In the productDeclarative Automation Bundles2026-03-16 In the examAgent Bricks: Multi-Agent SupervisorIn the productSupervisor Agent2026-02 In the examMosaic AI Agent FrameworkIn the productCustom agents2026 In the examMosaic AI Model Serving, Mosaic AI Vector Search, Mosaic AI Agent FrameworkIn the productModel Serving, AI Search, Agent Bricks2026 In the examOnline tablesIn the productOnline Feature Store2026

Design Applications

7 concepts

Exam guide objectives

Design a prompt that elicits a specifically formatted response
Select model tasks to accomplish a given business requirement
Select chain components for a desired model input and output
Translate business use case goals into a description of the desired inputs and outputs for the AI pipeline
Define and order tools that gather knowledge or take actions for multi-stage reasoning
Determine how and when to use Agent Bricks (Knowledge Assistant, Multiagent Supervisor, Information Extraction) to solve problems

Data Preparation

3 concepts

Exam guide objectives

Apply a chunking strategy for a given document structure and model constraints
Filter extraneous content in source documents that degrades quality of a RAG application
Choose the appropriate Python package to extract document content from provided source data and format
Define operations and sequence to write given chunked text into Delta Lake tables in Unity Catalog
Identify needed source documents that provide necessary knowledge and quality for a given RAG application
Use tools and metrics to evaluate retrieval performance
Design retrieval systems using advanced chunking strategies
Explain the role of re-ranking in the information retrieval process

Application Development

5 concepts

Exam guide objectives

Select LangChain or similar tools for use in a Generative AI application
Qualitatively assess responses to identify common issues such as quality and safety
Select a chunking strategy based on model and retrieval evaluation
Augment a prompt with additional context from a user's input based on key fields, terms, and intents
Create a prompt that adjusts an LLM's response from a baseline to a desired output
Implement LLM guardrails to prevent negative outcomes
Select the best LLM based on the attributes of the application to be developed
Select an embedding model context length based on source documents, expected queries, and optimization strategy
Select a model from a model hub or marketplace for a task based on model metadata and model cards
Select the best model for a given task based on common metrics generated in experiments
Utilize MLflow and Agent Framework for developing agentic systems
Compare the evaluation and monitoring phases of the Gen AI application life cycle
Enable multi-agent systems to leverage Genie Spaces or the conversational API to retrieve data

Assembling and Deploying Applications

8 concepts

Exam guide objectives

Code a chain using a pyfunc model with pre- and post-processing
Control access to resources from model serving endpoints
Code a simple chain according to requirements
Choose the basic elements needed to create a RAG application: model flavor, embedding model, retriever, dependencies, input examples, model signature
Register the model to Unity Catalog using MLflow
Create and query a Vector Search index
Identify how to serve an LLM application that leverages Foundation Model APIs
Explain the key concepts and components of Mosaic AI Vector Search
Identify batch inference workloads and apply ai_query() appropriately
Configure vector search for a particular solution based on number of embeddings, update frequency, latency, and cost requirements
Configure a persistent datastore to store and retrieve intermediate memory or structured information
Apply CI/CD best practices such as updating a Vector Search index, promoting prompts across environments, and testing individual components of an agent
Integrate managed, external, and custom MCP servers based on given application requirements
Apply prompt version control and manage the prompt lifecycle
Develop an appropriate interactive user-facing interface for an agent usage scenario (Apps, Slack, Teams, etc.)

Governance

4 concepts

Exam guide objectives

Use masking techniques as guardrails to meet a performance objective
Select guardrail techniques to protect against malicious user inputs to a Gen AI application
Use legal and licensing requirements for data sources to avoid legal risk
Recommend an alternative for problematic text mitigation in a data source feeding a GenAI application

Evaluation and Monitoring

7 concepts

Exam guide objectives

Select an LLM choice (size and architecture) based on a set of quantitative evaluation metrics
Select key metrics to monitor for a specific LLM deployment scenario
Evaluate agent performance using MLflow scoring and tracing
Use inference logging to assess deployed RAG application performance
Use Databricks features to control LLM costs
Use inference tables and Agent Monitoring to track a live LLM endpoint
Identify evaluation judges that require ground truth
Use AI Gateway (inference tables, usage tables, and rate limiting) to track an LLM or agent deployed via Agent Framework
Use Databricks custom scorers for evaluating agents and LLMs
Incorporate SME feedback to improve agent performance

How to use this roadmap

The six domains follow the order of the official exam guide (live as of March 18, 2026). No domain publishes a percentage weight in this version, so treat every section as equally likely and use the objective bullets, not the domain order, to judge how deep to go.

What makes this exam different from the data engineering tracks: it tests architecture judgment more than syntax. Expect scenario questions about picking a chunking strategy, choosing between a managed and a custom MCP server, or deciding when Agent Bricks beats a hand-built chain, often with more than one technically valid answer. Governance has no matching concept yet in this vault: PII masking as an LLM guardrail and licensing risk for training data are exam topics with no page to read here yet, so lean on the official documentation for that section.