Databricks-ML-Pro Model Development Practice Question
When using the Databricks Model Registry, what does a 'Model Version' represent in the context of the lifecycle?
⚠ Common exam trap
Candidates often confuse a 'Model Version' with the 'Registered Model' (the container) or a 'Run' (the training instance), failing to distinguish the immutable artifact snapshot.
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 unique, immutable snapshot of a specific model artifact and its metadata.
A Model Version is a point-in-time snapshot of a model, including the code, model weights, dependencies, and environment configuration. By tracking these versions, data scientists can compare performance across different iterations, rollback to previous states if a production deployment fails, and maintain an audit trail of how a model evolved over time. This versioning is foundational for reproducible machine learning.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
An automated report generated after every training run.
Why it's wrong here
A model version is a concrete artifact, not a report. While reports and metrics are logged during training, the version itself is a functional object stored in the registry that contains the serialised model and all its necessary supporting environment data for deployment purposes.
- ✓
A unique, immutable snapshot of a specific model artifact and its metadata.
Why this is correct
Each version in the Model Registry is immutable, ensuring that once a model is registered, it cannot be tampered with. This immutability is critical for compliance and reproducibility, as it guarantees that the exact model code and weights used in training are exactly what gets served in production.
- ✗
A live pointer that always points to the most recent training run.
Why it's wrong here
Versions are static, whereas aliases or tags are used to point to the 'latest' or 'production' models. A specific model version does not update automatically; if you need to update the model, you must create a new version, ensuring that previous deployments remain unaffected by new changes.
- ✗
A shared directory containing the raw training data and configuration files.
Why it's wrong here
Storing raw training data in the registry is poor practice due to volume constraints. Versions contain the serialized model, not the training data. Data lineage is tracked separately via tools like Unity Catalog or MLflow's data logging, not by bundling the raw dataset within the model's versioned artifact.
About these practice questions
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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-Pro 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-Pro exam.