Databricks-ML-Pro ML Ops Practice Question
Exhibit
{
"model": "churn_predictor",
"run_id": "d7a8e9f0",
"status": "READY",
"artifacts": {
"model_path": "/dbfs/mlflow/models/churn/1",
"conda_env": "environment.yaml"
}
}Refer to the exhibit. The deployment pipeline is failing to load the model artifact in the target production environment. What is the most likely cause?
⚠ Common exam trap
Many test-takers assume local or absolute DBFS paths are safe to use in deployment code, failing to recognize that MLflow URIs are required for environment portability.
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
✓
The path '/dbfs/...' is not a reliable way to reference artifacts across different clusters.
The exhibit shows a hard-coded path beginning with '/dbfs/'. In Databricks, accessing DBFS via local file system paths can be inconsistent across different clusters or environments. The correct approach is to use the MLflow URI (e.g., 'runs:/...') to load models, as it abstracts the underlying storage location and ensures the artifact is resolved correctly regardless of the execution environment or storage configuration.
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 version is not set to 'PRODUCTION' in the metadata.
Why it's wrong here
The status 'READY' refers to the registration state, not the lifecycle stage. While the stage might be set to 'PRODUCTION', the error is related to loading the artifact from the provided path, not the lifecycle status itself, which is a logic check, not an IO error.
- ✓
The path '/dbfs/...' is not a reliable way to reference artifacts across different clusters.
Why this is correct
Hard-coding DBFS paths is an anti-pattern. MLflow models should be referenced using the 'runs:/' or 'models:/' URI formats. These URIs are managed by the MLflow client and resolve correctly to the underlying artifact location, avoiding the path-resolution issues that occur when using literal file system paths.
- ✗
The conda_env file is missing from the artifact storage.
Why it's wrong here
If the file were missing, the error would specifically indicate a 'FileNotFoundException' for that file. The problem described is a general failure to load the model artifact, pointing towards the addressing method (the path) being incorrect rather than a missing specific configuration file.
- ✗
The run_id 'd7a8e9f0' has been archived and is no longer accessible.
Why it's wrong here
Archiving a run would typically trigger a specific 'AccessDenied' or 'RunNotFound' error. The issue here is related to the loading process of the artifact itself, implying the model exists but the method of referencing it is flawed, which is common with incorrect path usage.
Visual reference
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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.