Databricks-ML-Pro ML Ops Practice Question
Exhibit
MLflow Run ID: a1b2c3d4e5f6 Status: FAILED Error: [Databricks][MLflow] mlflow.exceptions.RestException: RESOURCE_DOES_NOT_EXIST: Model version with name 'customer_churn' and version '5' not found.
Refer to the exhibit. Your automated CI/CD pipeline triggered a model deployment to production, but the job failed with the error shown. What is the most likely cause?
⚠ Common exam trap
Candidates often blame infrastructure or network issues. However, in automated CI/CD, the most common failure is a race condition where the deployment script executes before the registration job finishes.
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 deployment job is referencing a model version that hasn't been created yet.
The error indicates that the deployment pipeline is attempting to access a model version that does not exist in the Model Registry. This usually happens when the pipeline triggers before the registration process completes or when a race condition occurs between the training job and the deployment job. Validating the existence of the model version before initiating deployment is essential for pipeline reliability.
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 was registered, but the cluster lacks permissions to read it.
Why it's wrong here
Permission errors typically result in an Access Denied or Forbidden exception, not a Resource Does Not Exist error. This specific error message confirms the registry cannot locate the requested object, suggesting a synchronization issue or a misconfigured reference to the model name or version.
- ✓
The deployment job is referencing a model version that hasn't been created yet.
Why this is correct
The REST exception confirms the system attempted to fetch a non-existent entity. In CI/CD, this suggests a dependency failure where the deployment script executes before the training job successfully completes the registration, indicating a need for improved job orchestration or explicit completion gating.
- ✗
The model serving endpoint is already occupied by another model version.
Why it's wrong here
Model serving endpoints can be updated regardless of existing models. If the endpoint were occupied, you would receive a conflict error or the system would overwrite the configuration, not an error stating that a specific registry version does not exist in the database.
- ✗
The training cluster ran out of memory during the model artifact upload.
Why it's wrong here
An out-of-memory error would result in a cluster failure or a connection timeout error. It would not manifest as a specific REST exception indicating that a named model version is missing from the registry, which is a logic-level error rather than a resource-level error.
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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.