Databricks-ML-Assoc Model Development Practice Question
A machine learning engineer is using MLflow to log a model trained with XGBoost. They want to ensure that the model can be loaded and used for inference in a different environment without requiring the original training environment. Which MLflow feature allows the model to capture its dependencies and environment?
⚠ Common exam trap
The trap here is thinking that the model signature or version handles environment portability; only the MLmodel file with dependency specifications does that.
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
✓
MLmodel file with conda_env and requirements.txt
The MLmodel file, along with conda_env and requirements.txt, captures the dependencies required to load and run the model. When logging a model, MLflow automatically generates these files based on the current environment. This enables the model to be deployed in a different environment by recreating the necessary dependencies, ensuring consistent behavior.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
MLmodel file with conda_env and requirements.txt
Why this is correct
When logging a model, MLflow automatically creates an MLmodel file that includes a conda environment specification and a requirements.txt file. These files list the dependencies needed to recreate the environment. This allows the model to be loaded in a different environment by installing the specified packages, ensuring reproducibility.
- ✗
Run ID
Why it's wrong here
The run ID identifies the MLflow run but does not contain any environment details. It is used to retrieve artifacts, but the environment specification is stored within the model artifact itself, not in the run ID. Relying on the run ID alone would not provide the necessary dependencies for loading the model elsewhere.
- ✗
Model signature
Why it's wrong here
The model signature defines the input and output schema but does not capture the environment or dependencies. It is essential for validating data but does not help in recreating the runtime environment. Without dependency information, the model might fail to load due to missing libraries even if the signature is correct.
- ✗
Model version
Why it's wrong here
The model version is a registry concept that tracks iterations of a registered model. It does not include environment dependencies. While versioning helps manage model lifecycle, it does not address the need to recreate the environment for inference. The MLmodel file is the component that carries dependency information.
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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-ML-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-ML-Assoc exam.