Databricks-ML-Assoc Model Development Practice Question
When logging a machine learning model using MLflow, which component is required to capture the environment dependencies (such as library versions) to ensure the model can be reproduced in a different Databricks workspace?
⚠ Common exam trap
Test-takers frequently assume that the trained model binary or weights alone are sufficient for cross-workspace reproduction, forgetting that environment dependencies must be explicitly captured in configuration files like conda.yaml.
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
✓
A saved requirements file or conda environment definition.
The MLflow 'conda.yaml' or 'requirements.txt' file is the standardized mechanism for tracking environment dependencies. By logging these alongside the model artifact, MLflow creates a reproducible environment signature. This is critical for MLOps, as it ensures that the model runs against the exact versions of libraries it was trained on, preventing runtime errors caused by mismatched dependency versions across different development, staging, and production environments.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The model's training accuracy metrics.
Why it's wrong here
Metrics like accuracy, precision, and recall are used to evaluate model performance, not to reproduce the environment. While they are useful for comparing models, they do not contain any information regarding the software packages, versions, or Python configurations required to execute the model code successfully.
- ✓
A saved requirements file or conda environment definition.
Why this is correct
A requirements file or conda definition explicitly lists every package and version dependency used during the training process. MLflow uses these files during the model deployment phase to reconstruct an identical environment, ensuring that the model's inference logic executes correctly without missing dependencies or incompatible library versions.
- ✗
The raw training dataset used during training.
Why it's wrong here
Storing raw training data is related to data lineage, but it is not a requirement for the runtime environment. Reproducing the model environment depends on the software stack and library versions, not the specific records that were used to fit the model parameters during the training phase.
- ✗
The Spark configuration file (spark-defaults.conf).
Why it's wrong here
The Spark configuration file manages cluster settings such as executor memory and parallelism. While cluster tuning affects performance, it does not define the Python or R library dependencies needed for the model code itself. Restoring the Spark config does not guarantee that the required machine learning libraries exist.
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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.