Databricks Certified Generative AI Engineer Associate Preparation Details
The Databricks Certified Generative AI Engineer Associate exam tests your ability to design, build, and deploy LLM powered applications on Databricks. This guide maps every exam objective to official documentation covering prompt engineering, RAG pipelines, Vector Search, MLflow, and governance. You can also explore more Databricks certification study guides on the Data Engineering category to keep building your skills.
Databricks Certified Generative AI Engineer Associate Materials
| Coursera | Databricks GenAI Engineering |
| Udemy | Databricks Generative AI Engineer Associate Exam Prep |
| Whizlabs | Databricks Certified Generative AI Engineer Associate |
Section 1: Design Applications (14% of scored content)
Design Applications
Design a prompt that elicits a specifically formatted response
Structured outputs on Databricks
Evaluate and compare prompt versions
Function calling on Databricks
Select model tasks to accomplish a given business requirement
Databricks Foundation Model APIs
Databricks-hosted foundation models available in Foundation Model APIs
Concepts: Generative AI on Databricks
Select chain components for a desired model input and output
LangChain on Databricks for LLM development
Translate business use case goals into a description of the desired inputs and outputs for the AI pipeline
Generative AI app developer workflow
Databricks generative AI capabilities
Concepts: Generative AI on Databricks
Define and order tools that gather knowledge or take actions for multi-stage reasoning
Create AI agent tools using Unity Catalog functions
Get started: Query LLMs and prototype AI agents with no code
Determine how and when to use Agent Bricks (Knowledge Assistant, Multiagent Supervisor, Information Extraction) to solve problems
Use Knowledge Assistant to create a high-quality chatbot over your documents
Use Supervisor Agent to create a coordinated multi-agent system
Use Agent Bricks: Information Extraction
Section 2: Data Preparation (14% of scored content)
Data Preparation
Apply a chunking strategy for a given document structure and model constraints
Build an unstructured data pipeline for RAG
AI Search retrieval quality guide
Intelligent document processing
Filter extraneous content in source documents that degrades quality of a RAG application
Build an unstructured data pipeline for RAG
Improve RAG application quality
Document Intelligence powered by AI Functions
Choose the appropriate Python package to extract document content from provided source data and format.
Work with unstructured data in volumes
Intelligent document processing
Define operations and sequence to write given chunked text into Delta Lake tables in Unity Catalog
Tutorial: Create and manage Delta Lake tables
Unity Catalog managed tables for Delta Lake and Apache Iceberg
Databricks Unity Catalog table types
Identify needed source documents that provide necessary knowledge and quality for a given RAG application
Build an unstructured data pipeline for RAG
Improve RAG application quality
AI Search retrieval quality guide
Use tools and metrics to evaluate retrieval performance
Assess performance: Metrics that matter
AI Search retrieval quality guide
Design retrieval systems using advanced chunking strategies
AI Search retrieval quality guide
Build an unstructured data pipeline for RAG
Explain the role of re-ranking in the information retrieval process
AI Search retrieval quality guide
Section 3: Application Development (30% of scored content)
Application Development
Select Langchain/similar tools for use in a Generative AI application.
LangChain on Databricks for LLM development
Integrate Unity Catalog tools with third party generative AI frameworks
Qualitatively assess responses to identify common issues such as quality and safety
Select chunking strategy based on model & retrieval evaluation
Build an unstructured data pipeline for RAG
Assess performance: Metrics that matter
AI Search retrieval quality guide
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
Evaluate and compare prompt versions
Track prompt versions alongside application versions
Implement LLM guardrails to prevent negative outcomes
Configure guardrails for Unity AI Gateway endpoints
Select the best LLM based on the attributes of the application to be developed
Databricks-hosted foundation models available in Foundation Model APIs
LLMOps workflows on Databricks
Select an embedding model context length based on source documents, expected queries, and optimization strategy
Foundation model REST API reference
Databricks-hosted foundation models available in Foundation Model APIs
Register and serve an OSS embedding model
Select a model from a model hub or marketplace for a task based on model metadata/model cards
Access generative AI and LLM models from Unity Catalog
What is Databricks Marketplace?
Access data products in Databricks Marketplace
Select the best model for a given task based on common metrics generated in experiments
Tutorial: Evaluate and improve a GenAI application
Utilize MLflow and Agent Framework for developing agentic systems
Compare the evaluation and monitoring phases of the Gen AI application life cycle
Generative AI app developer workflow
Enable multi-agent systems to leverage Genie Spaces or conversational API to retrieve data
Connect agents to structured data
Section 4: Assembling and Deploying Applications (22% of scored content)
Assembling and Deploying Applications
Code a chain using a pyfunc model with pre- and post-processing
Control access to resources from model serving endpoints
Manage model serving endpoints
Create custom model serving endpoints
Code a simple chain according to requirements
LangChain on Databricks for LLM development
Choose the basic elements needed to create a RAG application: model flavor, embedding model, retriever, dependencies, input examples, model signature
Connect agents to unstructured data
Register the model to Unity Catalog using MLflow
Manage model lifecycle in Unity Catalog
Migrate workflows and models to Unity Catalog
Log, load, and register MLflow models
Create and query a Vector Search index
Create vector search endpoints and indexes
Identify how to serve an LLM application that leverages Foundation Model APIs
Databricks Foundation Model APIs
Deploy models using Model Serving
Explain the key concepts and components of Mosaic AI Vector Search
Create vector search endpoints and indexes
Identify batch inference workloads and apply ai_query() appropriately
Deploy models for batch inference and prediction
Configure vector search for a particular solution based on number of embeddings, update frequency, latency, and cost requirements.
AI Search retrieval quality guide
Create vector search endpoints and indexes
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.
Evaluate and compare prompt versions
Best practices and recommended CI/CD workflows on Databricks
Integrate managed, external, and custom MCP servers based on a given application requirements
Model Context Protocol (MCP) on Databricks
Databricks managed MCP servers
Install an external MCP server
Host custom MCP servers using Databricks apps
Apply prompt version control and manage prompt lifecycle
Evaluate and compare prompt versions
Track prompt versions alongside application versions
Develop an appropriate interactive user facing interface for an agent usage scenario (Apps, Slack, Teams, etc.)
Author an AI agent and deploy it on Databricks Apps
Build and share a chat UI with Databricks Apps
Section 5: Governance (8% of scored content)
Governance
Use masking techniques as guard rails to meet a performance objective
Common patterns for row filtering and column masking
Select guardrail techniques to protect against malicious user inputs to a Gen AI application
Configure guardrails for Unity AI Gateway endpoints
Use legal/licensing requirements for data sources to avoid legal risk
Applicable model developer terms
What is Databricks Marketplace?
Recommend an alternative for problematic text mitigation in a data source feeding a GenAI application
Build an unstructured data pipeline for RAG
Configure guardrails for Unity AI Gateway endpoints
Section 6: Evaluation and Monitoring (12% of scored content)
Evaluation and Monitoring
Select an LLM choice (size and architecture) based on a set of quantitative evaluation metrics
Conduct your own LLM endpoint benchmarking
Assess performance: Metrics that matter
Databricks-hosted foundation models available in Foundation Model APIs
Select key metrics to monitor for a specific LLM deployment scenario
Monitor usage for AI Gateway endpoints
Assess performance: Metrics that matter
Evaluate agent performance using MLflow scoring and tracing
Tutorial: Evaluate and improve a GenAI application
Use inference logging to assess deployed RAG application performance
Monitor served models using AI Gateway-enabled inference tables
Monitor models using inference tables
Use Databricks features to control LLM costs
Configure rate limits for AI services using Unity AI Gateway
Monitor usage for AI Gateway endpoints
Use inference tables and Agent Monitoring to track a live LLM endpoint
Monitor served models using AI Gateway-enabled inference tables
Configure AI Gateway on model serving endpoints
Identify evaluation judges that require ground truth
Migrate to MLflow 3 from Agent Evaluation
Use AI Gateway (Inference Tables, Usage Tables, and rate limiting) to track an LLM or agent deployed via Agent Framework.
Monitor served models using AI Gateway-enabled inference tables
Monitor usage for AI Gateway endpoints
Configure rate limits for AI services using Unity AI Gateway
Use Databricks custom Scorers for evaluating agents and LLMs
Incorporate SME feedback to improve agent performance
Collect feedback and expectations by labeling existing traces
Create and manage labeling sessions
Wrapping Up Databricks Certified Generative AI Engineer Associate
This guide covered every domain of the Databricks Certified Generative AI Engineer Associate exam, from prompt design and RAG data preparation to Vector Search, MLflow evaluation, and AI Gateway governance. Working through each official documentation link will help you build hands on experience with the tools this certification expects you to know. You can also explore more Databricks certification study guides on the Data Engineering category to keep building your skills. Have a question or tip? Leave a comment below.
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