Databricks-GenAI-Assoc Application Development Practice Question
A developer needs to deploy a custom Python model that requires non-standard library dependencies. Which MLflow feature should the developer use to specify these environment requirements during model logging?
⚠ Common exam trap
Candidates often assume the environment is automatically captured from the local machine. They fail to explicitly define 'pip_requirements' or 'conda_env', causing deployment failures when the server lacks local dependencies.
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 environment requirements (pip_requirements)
When logging a custom model with MLflow, the developer should use the `pip_requirements` or `conda_env` parameter in the `mlflow.pyfunc.log_model` function. This ensures that the environment is correctly packaged and reproducible. During deployment, the Databricks Model Serving service reads these requirements to recreate the identical software environment, preventing runtime errors caused by missing dependencies when the model is loaded and served in the production environment.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Model Schema definition
Why it's wrong here
The model schema describes the input and output data types expected by the model. It is essential for validation and documentation, but it does not specify or manage the underlying software dependencies required to run the model code. Therefore, it cannot solve issues related to missing library imports.
- ✓
MLflow environment requirements (pip_requirements)
Why this is correct
By explicitly providing a list of required libraries via the `pip_requirements` argument during the logging process, you guarantee that the Model Serving environment will install them before loading the model. This is the industry-standard way to ensure that complex, custom model code runs successfully in production.
- ✗
Global workspace library settings
Why it's wrong here
Global workspace library settings affect all clusters in the workspace. Using them for model-specific dependencies is a poor practice that leads to library conflicts and version management issues. Dependencies should be bundled with the model to ensure isolation and consistent behavior across different environments and model versions.
- ✗
Custom model signatures
Why it's wrong here
Model signatures define the input structure of the model for inference. While important for type checking, they do not influence the environment runtime configuration. They are completely independent of the software dependencies required to execute the Python code associated with the model's prediction function during the inference process.
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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
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