Databricks-ML-Assoc ML Workflows Practice Question
Exhibit
Error: mlflow.exceptions.RestException: RESOURCE_DOES_NOT_EXIST: Model version with name 'demand_forecast' and version '5' not found.
Refer to the exhibit. A data scientist receives this error while trying to load a model. What is the most likely cause of this failure in the workflow?
⚠ Common exam trap
Candidates often assume the error is related to network connectivity or permissions, failing to notice that hard-coded version numbers are brittle and prone to breaking when versions are updated.
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 model version number is incorrectly referenced in the code.
This error indicates that the code is attempting to reference a specific version of a model that does not exist in the Model Registry. This often occurs when a script relies on hard-coded version numbers rather than dynamic lookups or stage-based references. In a production workflow, using stage aliases like 'Production' is preferred to ensure that the application always fetches the current valid version without needing manual updates when models are updated.
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 has been deleted from the registry.
Why it's wrong here
While deletion is possible, it is less common than simple versioning errors. The error specifically flags that version 5 cannot be found, which typically happens when a script is misaligned with the current state of the model registry or references a version that was never registered successfully.
- ✓
The model version number is incorrectly referenced in the code.
Why this is correct
The error explicitly states that the version does not exist. This is a common issue when using hard-coded version integers. If the model was never registered as version 5, or if it was deleted, the retrieval command will fail, confirming the code is pointing to a non-existent artifact.
- ✗
The cluster does not have permission to access the MLflow registry.
Why it's wrong here
Insufficient permissions would typically result in a 'Permission Denied' or 'Access Forbidden' error rather than a 'Resource Does Not Exist' error. The system is able to reach the registry but cannot locate the specific model object, pointing to an issue with the identifier rather than access rights.
- ✗
The MLflow tracking URI is pointing to a different workspace.
Why it's wrong here
If the tracking URI were incorrect, the system would likely fail to connect or return a global registry error. The fact that it returns a specific model name and version error implies the client is successfully communicating with the intended registry but searching for a nonexistent item.
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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.