Chain Packaging, pyfunc, RAG, Vector Search and Serving
Deployment covers packaging chains, registering artifacts, combining RAG components, connecting Vector Search, and serving models or apps reliably.
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
A chain can be packaged and logged so the prompt, retriever, tools, model endpoint, and output parser deploy as a reproducible artifact.
Exam cue: Package the chain with its dependencies so staging and production run the same logic.
Concept 2
RAG deployment requires source refresh, embedding jobs, vector index synchronization, retrieval filters, and model-serving integration.
Exam cue: Keep the vector index synchronized with governed source data and embeddings.
Concept 3
Model Serving should be selected and configured around latency, throughput, scale, endpoint permissions, monitoring, and cost.
Exam cue: Use endpoint permissions and serving configuration to control production access.
Risk pitfalls and guardrails
Deploying notebook code manually without logging the chain artifact.
Guardrail: Avoid overusing agents, skipping retrieval evaluation, ignoring Unity Catalog permissions, or promoting prompt changes outside release control.
Refreshing source data without updating embeddings or the vector index.
Guardrail: Avoid overusing agents, skipping retrieval evaluation, ignoring Unity Catalog permissions, or promoting prompt changes outside release control.
Making a model endpoint public when only a service principal or app should call it.
Guardrail: Avoid overusing agents, skipping retrieval evaluation, ignoring Unity Catalog permissions, or promoting prompt changes outside release control.
Memory anchors
Logged Chain
A logged chain captures prompts, retriever configuration, tools, model calls, and parser logic as an artifact.
pyfunc Wrapper
A pyfunc wrapper packages custom Python inference logic behind a standard model interface.
RAG Pipeline
A RAG pipeline retrieves source evidence and passes it to the model for grounded generation.
Vector Index Sync
Vector index sync keeps searchable embeddings aligned with current source data and metadata.
Serving Endpoint
A serving endpoint exposes a model or chain for application calls with scale and access controls.
Service Principal
A service principal can give applications controlled non-human access to Databricks resources.
Endpoint Permission
Endpoint permission controls who or what can invoke a production serving endpoint.
Deployment Artifact
A deployment artifact is the versioned package promoted to staging or production.
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 custom RAG application must be deployed behind a Databricks Model Serving endpoint. What should be logged as the deployable unit?
A chain works in a notebook because a global variable contains the retriever. After logging, Model Serving cannot find it. What is the best fix?
Answer all questions to submit.
Next step personalized recommendations
Continue learning
Move forward only after this module is stable.
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