Databricks-ML-Assoc Databricks Machine Learning Practice Question
Exhibit
Error: mlflow.exceptions.RestException: RESOURCE_DOES_NOT_EXIST: Model 'production_model' not found in registry.
Refer to the exhibit. A user attempts to transition a model to production, but the code fails with the provided error. What is the most likely cause?
⚠ Common exam trap
Users often assume that logging a model with mlflow.log_model automatically registers it in the Model Registry. They forget that registration is a separate, explicit step required for lifecycle management.
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.
The error indicates that the Model Registry cannot locate the model name provided. In Databricks, models must be registered before they can be transitioned to specific stages like 'Production'. The user likely misspelled the model name or is attempting to reference a model that has not yet been registered within the current workspace environment. Verifying the Model Registry UI will confirm the correct naming conventions and existing model registrations.
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 sufficient permissions to read the model.
Why it's wrong here
Permission issues typically trigger 'PERMISSION_DENIED' errors rather than 'RESOURCE_DOES_NOT_EXIST'. If the user lacked access, the API would explicitly state that the identity is unauthorized, rather than claiming the resource is missing entirely from the registry's namespace for the current user's session.
- ✓
The model has not been registered in the Model Registry.
Why this is correct
The error indicates the registry cannot find the entity 'production_model'. This signifies that no model with that specific name exists within the registry. The user must first register the model or ensure they are pointing to the correct registered model name before calling transition operations.
- ✗
The model version is archived and cannot be transitioned.
Why it's wrong here
If a version were archived, the error would typically relate to version status or state transitions rather than the model container itself being missing. 'RESOURCE_DOES_NOT_EXIST' is a fundamental lookup failure, implying the registry has no knowledge of the base model name specified by the user.
- ✗
The model artifact is missing from the underlying DBFS storage.
Why it's wrong here
Even if physical artifacts were missing, the Model Registry entry would still exist as an object. The error message explicitly points to the registry lookup failing. The registry manages the metadata and pointers to the artifacts, and the lookup failure happens before the system attempts to resolve file paths.
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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.