Coding a simple chain to a given set of requirements

A simple LLM chain in Databricks typically composes a prompt template, a model endpoint call, and an output parser using LangChain (or a similar orchestration framework) wired to a Databricks-served endpoint (e.g., Foundation Model API or a custom-served model). The exam expects you to know the basic building blocks and how they're chained together, not deep customization details.

1 · Learn the must-know

• 1Chains are usually built with LangChain's LCEL (pipe syntax: prompt | llm | output_parser) or the legacy LLMChain class, connecting a PromptTemplate to a chat/completion model.
• 2The LLM component in the chain typically points to a Databricks Model Serving endpoint using the ChatDatabricks or Databricks LLM wrapper class, requiring an endpoint name.
• 3Input variables in the PromptTemplate must match the keys passed into the chain's invoke/run call, or you'll get a missing variable error.
• 4Output parsers (e.g., StrOutputParser) convert raw model output into the desired format for downstream use, such as plain text or structured JSON.
• 5Chains should be tested locally (e.g., via invoke() or run()) before logging with MLflow and deploying, to catch prompt/variable mismatches early.
• 6When the chain will be deployed via Model Serving, it should be logged as an MLflow pyfunc/LangChain flavor model so it can be registered and served consistently.

2 · Check your understanding