CI/CD, MCP, Prompts and Interfaces
Assembling applications includes promoting prompts and chains, versioning interfaces, using MCP for tools, and operating Databricks Apps or APIs.
How to study for the Databricks Generative AI Engineer Associate exam
Treat each item as a production GenAI decision: define the task, prepare governed data, build the prompt or agent, package deployment, then evaluate, monitor, and control risk.
Core concepts
Concept 1
CI/CD for generative AI should version code, prompts, retriever settings, evaluation sets, tool schemas, and deployment configuration.
Exam cue: Treat prompt and retriever changes as release-managed assets.
Concept 2
MCP can standardize how agents discover and call tools or context providers across systems.
Exam cue: Use MCP when tool integration needs a standard protocol rather than bespoke wiring.
Concept 3
Application interfaces should expose stable inputs, outputs, authentication, error handling, and feedback collection paths.
Exam cue: Add feedback capture to user-facing interfaces so monitoring and evaluation improve.
Risk pitfalls and guardrails
Shipping prompt changes outside source control and promotion rules.
Guardrail: Avoid overusing agents, skipping retrieval evaluation, ignoring Unity Catalog permissions, or promoting prompt changes outside release control.
Changing tool input schemas without updating agent tests and interface contracts.
Guardrail: Avoid overusing agents, skipping retrieval evaluation, ignoring Unity Catalog permissions, or promoting prompt changes outside release control.
Building a chat interface with no way to collect user feedback or trace IDs.
Guardrail: Avoid overusing agents, skipping retrieval evaluation, ignoring Unity Catalog permissions, or promoting prompt changes outside release control.
Memory anchors
Prompt Release
A prompt release versions instructions, variables, examples, and evaluation evidence before promotion.
Retriever Setting
A retriever setting controls indexes, filters, top-k, reranking, and query transformation.
Tool Contract
A tool contract defines stable inputs, outputs, errors, auth, and side effects for model use.
MCP Server
An MCP server exposes tools or context through a standardized protocol for agents.
Interface Contract
An interface contract defines request fields, response shape, auth behavior, and error handling.
Databricks App
A Databricks App can host an internal application that calls governed Databricks resources.
CI/CD Gate
A CI/CD gate blocks promotion when tests, evaluation metrics, security checks, or approvals fail.
Feedback Capture
Feedback capture records user ratings, comments, corrections, and trace references for improvement.
Checkpoint rule
Do the check-up only after you can summarize each concept in one sentence and identify one dangerous pitfall from memory.
Knowledge Check (after reading)
Short check-up to confirm understanding of this module.
Check-up Questions
A CI pipeline builds a GenAI application. Which artifacts should be pinned for a reproducible release?
A pull request changes a system prompt. What should block merge if the prompt is production-critical?
Answer all questions to submit.
Next step personalized recommendations
Continue learning
Move forward only after this module is stable.
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