Databricks Certified Generative AI Engineer Associate Exam Prep

This section covers how to design LLM-enabled applications in Databricks by translating business requirements into prompts, model tasks, chain components, and AI pipeline inputs/outputs. It also includes selecting and ordering tools for multi-stage reasoning and deciding when to use Agent Bricks capabilities.

• Design a prompt that elicits a specifically formatted response
• Design prompts for specific response formats
• Select model tasks to accomplish a given business requirement
• Match model tasks to business needs
• Select chain components for a desired model input and output
• Choose chain components for inputs and outputs
• Translate business use case goals into a description of the desired inputs and outputs for the AI pipeline

Key references: GenAI Associate official exam guide · ExamPal shared topic tree

This section covers preparing source data for retrieval-augmented generation (RAG) workflows, including chunking, filtering noisy content, selecting extraction tools, and loading chunked text into Delta Lake tables in Unity Catalog. It also addresses source document selection, retrieval evaluation, advanced chunking strategies, and the role of re-ranking in retrieval systems.

• 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

Key references: GenAI Associate official exam guide · ExamPal shared topic tree

Covers practical skills for building generative AI applications, including tool selection, prompt construction, retrieval design, model selection, guardrails, and evaluation/monitoring. It also includes agentic and multi-agent system development using MLflow, Agent Framework, Genie Spaces, and conversational APIs.

• Select Langchain/similar tools for use in a Generative AI application
• Qualitatively assess responses to identify common issues such as quality and safety
• Select chunking strategy based on model & 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

Key references: GenAI Associate official exam guide · ExamPal shared topic tree

Covers how to assemble AI applications using chains, retrieval, vector search, and model serving patterns. It also includes deployment, CI/CD, prompt lifecycle management, MCP server integration, and user-facing interfaces for agent scenarios.

• 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

Key references: GenAI Associate official exam guide · ExamPal shared topic tree

This section covers governance practices for GenAI applications, with emphasis on guardrails, masking techniques, and mitigation strategies that support performance objectives and reduce risk. It also addresses protecting applications from malicious user inputs and ensuring data sources comply with legal and licensing requirements.

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

Key references: GenAI Associate official exam guide · ExamPal shared topic tree

Covers how to evaluate LLMs and agents, choose metrics, and monitor deployed applications in Databricks. It also includes cost control, inference logging, AI Gateway, custom scorers, and incorporating SME feedback to improve performance.

• Select an LLM choice (size and architecture) based on a set of quantitative evaluation metrics
• 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
• Configure vector search for a particular solution based on number of embeddings, update frequency, latency, and cost requirements
• 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

Key references: GenAI Associate official exam guide · ExamPal shared topic tree