Databricks-ML-Pro Model Development Practice Question
A machine learning engineer wants to ensure that model training artifacts are persistent and accessible even if the ephemeral compute cluster is terminated. What is the standard practice in Databricks for achieving this?
⚠ Common exam trap
Candidates assume that saving files to the local file system is sufficient. They ignore the fact that cluster storage is ephemeral and will be wiped upon termination.
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
✓
Log the model using MLflow, which stores the artifact in the configured backend.
In Databricks, local cluster storage is ephemeral and is deleted when the cluster terminates. To ensure model artifacts persist, developers must log them to MLflow, which automatically handles the backend storage in DBFS or Unity Catalog-managed storage. This practice decouples the model lifecycle from the compute lifecycle, ensuring that models remain available for deployment or further evaluation after the compute resources are released.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Save the model to the local file system using the /dbfs/ path prefix.
Why it's wrong here
While /dbfs/ allows access to distributed storage, manually managing model paths is error-prone. MLflow provides an abstraction layer that handles path management and artifact tracking, ensuring metadata and lineage are correctly stored. Relying on manual file paths bypasses the built-in tracking features that make models deployable.
- ✓
Log the model using MLflow, which stores the artifact in the configured backend.
Why this is correct
MLflow handles the persistence of model artifacts by automatically uploading them to the managed storage backend (e.g., DBFS or Unity Catalog). This approach ensures that the model is versioned, tracked, and accessible across different clusters or users, maintaining the persistence required for production-grade machine learning lifecycle management.
- ✗
Export the model artifact to a temporary workspace file and download it locally.
Why it's wrong here
Manual downloading to a local machine is not a scalable production practice. It creates a siloed environment where the model is no longer managed by Databricks, losing the benefits of version control, lineage tracking, and automated deployment pipelines provided by the integrated MLflow Model Registry.
- ✗
Configure the cluster to use an external persistent volume for local storage.
Why it's wrong here
Databricks compute clusters are designed to be ephemeral. Attempting to force persistence via external volume mounting on the local node is a non-standard workaround that is not supported or recommended. The correct approach is to utilize the platform's native artifact storage features via MLflow's tracking URI.
About these practice questions
This Databricks-ML-Pro question is part of Courseiva's 300-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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.