Databricks-GenAI-Assoc Application Development Practice Question
An AI engineer is building a RAG application and wants to log the retrieval context and generated response for each request to MLflow for evaluation. They are using the `mlflow.langchain` flavor. Which method should they use to log the model so that MLflow automatically captures the necessary artifacts?
⚠ Common exam trap
The trap here is assuming that any logging function works, but only the flavor-specific logger for LangChain automatically captures the necessary artifacts for RAG evaluation.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
mlflow.langchain.log_model
For LangChain models, `mlflow.langchain.log_model` is the specialized logging function that captures the chain's configuration and enables MLflow to track inputs and outputs. This allows the engineer to later evaluate retrieval context and responses using MLflow's evaluation capabilities. Other logging methods do not provide this automatic integration.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
mlflow.sklearn.log_model
Why it's wrong here
`mlflow.sklearn.log_model` is for scikit-learn models and does not support LangChain. It cannot log the chain or its retrieval steps. This method is entirely inappropriate for a RAG application built with LangChain, as it lacks the necessary hooks to capture the context and response.
- ✗
mlflow.tensorflow.log_model
Why it's wrong here
`mlflow.tensorflow.log_model` is for TensorFlow models and is irrelevant for LangChain. It would not capture the LangChain chain's components or the retrieval context. Using it would require significant custom code and would not integrate with MLflow's LangChain evaluation features.
- ✓
mlflow.langchain.log_model
Why this is correct
`mlflow.langchain.log_model` is designed specifically for LangChain models. It automatically logs the chain's structure, prompts, and other artifacts, enabling MLflow to capture inputs and outputs for evaluation. This method simplifies logging and ensures compatibility with MLflow's evaluation tools, making it the correct choice.
- ✗
mlflow.pyfunc.log_model
Why it's wrong here
`mlflow.pyfunc.log_model` is used for custom Python models and does not automatically capture LangChain-specific artifacts like the chain's configuration or prompts. While it can log the model, it requires manual instrumentation to capture retrieval context and responses. It is not the most efficient choice for LangChain models.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
This Databricks-GenAI-Assoc practice question is part of Courseiva's free Databricks certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Databricks-GenAI-Assoc exam.