Databricks-ML-Assoc Databricks Machine Learning Practice Question
Exhibit
Error: mlflow.exceptions.RestException: RESOURCE_DOES_NOT_EXIST: Model version with name 'fraud_model' and version '5' not found.
Refer to the exhibit. A data scientist is attempting to deploy a model using the MLflow client. The error above occurs during the deployment script. What is the most likely cause of this failure?
⚠ Common exam trap
Candidates often assume that simply training a model makes it available for serving. They overlook the mandatory step of explicitly registering the model in the MLflow Model Registry first.
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 has not been registered in the Model Registry with the specified name and version.
This error indicates that the specified model version does not exist in the Registry, either because it was never registered, was deleted, or the name/version number was referenced incorrectly. In a Databricks context, verify that the model has been successfully logged and registered using mlflow.register_model. This is critical for deployment pipelines as it validates that the artifact exists before the serving infrastructure attempts to load the model file.
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 cluster does not have the necessary MLflow libraries installed.
Why it's wrong here
If the libraries were missing, the error would likely be an ImportError or AttributeError during the script execution, not a RestException from the MLflow tracking server. A RestException explicitly suggests the API call reached the server but the requested resource could not be located in the registry.
- ✓
The model has not been registered in the Model Registry with the specified name and version.
Why this is correct
The RestException explicitly confirms that the registry lookup failed for the provided name and version. This indicates a mismatch between the deployment configuration and the actual state of the registry. The user must verify the registration status via the UI or list_model_versions to confirm existence.
- ✗
The user lacks permissions to read the model version from the Registry.
Why it's wrong here
A permission error typically results in a 403 Forbidden status code rather than a Resource Does Not Exist error. While access control is important, the specific nature of this error points toward a missing resource rather than an authentication or authorization failure within the Databricks workspace environment.
- ✗
The model version is still in the 'Archived' state and cannot be accessed.
Why it's wrong here
Even archived models exist in the registry and can be retrieved by their specific version identifier. The error message indicates the resource itself is missing, not that it is in an invalid state for the requested operation, which would usually trigger a different set of error messages.
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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.