Databricks-ML-Assoc Databricks Machine Learning Practice Question
Exhibit
{ "name": "my_model", "version": 1, "status": "PENDING_REGISTRATION" }Refer to the exhibit. A user attempts to load a model using the MLflow Python API, but the load fails. Based on the JSON snippet, what is the most likely issue?
⚠ Common exam trap
Candidates often assume the error is due to authentication or missing permissions. They fail to check the model status, which clearly indicates it is still in a pending state.
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 is not in a ready state for inference.
The model status is listed as 'PENDING_REGISTRATION'. Models in this state are not ready for use because they are still being processed or finalized by the backend. MLflow will prevent loading models that haven't successfully completed registration. The user must wait for the registration process to complete, changing the status to a ready state, before the model artifact can be successfully loaded and used for inference.
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 name is misspelled in the load request.
Why it's wrong here
The JSON output confirms the existence of 'my_model'. If the name were misspelled, the system would typically return a 'RESOURCE_DOES_NOT_EXIST' error. The fact that the status is returned means the reference is valid, but the object is not ready for retrieval by the client application.
- ✓
The model is not in a ready state for inference.
Why this is correct
The 'PENDING_REGISTRATION' status explicitly indicates that the model object is currently undergoing registration procedures. It has not been fully ingested by the Model Registry service, meaning the artifact repository is not yet ready to serve the model for loading or inference operations until the transition completes.
- ✗
The user lacks permissions to read the model.
Why it's wrong here
Permission errors would prevent the user from even querying the metadata returned in the snippet. Since the JSON is visible to the user, they have sufficient read access to the registry. The problem is with the state of the model itself, not the user's authorization to view it.
- ✗
The version number provided is outdated.
Why it's wrong here
The status 'PENDING_REGISTRATION' is independent of the version number. Even if the version were older than the latest, the registry would still serve it if it were properly registered. The issue is the transient state of the model, which prevents any version from being loaded at this time.
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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.