Databricks-ML-Pro ML Ops Practice Question
You are building a Databricks job that trains a model and registers it to the MLflow Model Registry. After registration, you need to automatically transition the model version to 'Staging' only if its validation accuracy exceeds 0.95. The job should fail if the accuracy is below this threshold. Which approach should you implement?
⚠ Common exam trap
The trap here is assuming that webhooks or registration-time stage settings can enforce a metric threshold, when they actually operate after or independently of metric evaluation.
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
✓
Use the MLflow Client to register the model, then use a conditional step in the job to call the transition_model_version_stage method if the accuracy metric meets the threshold.
To conditionally transition a model version based on a metric, you need to programmatically retrieve the metric and then call the MLflow Client's transition method. This allows you to enforce a threshold and fail the job if the condition is not met. Webhooks and REST API calls lack the ability to evaluate metrics before transitioning, and setting the stage during registration does not incorporate a conditional check.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use the MLflow Client to register the model, then use a conditional step in the job to call the transition_model_version_stage method if the accuracy metric meets the threshold.
Why this is correct
The MLflow Client provides programmatic control over model versions, including transition_model_version_stage. By retrieving the run's metrics and conditionally transitioning only when accuracy exceeds 0.95, you enforce the business rule. If the condition fails, you can raise an exception to fail the job. This approach is flexible and integrates well with Databricks jobs.
- ✗
Configure the MLflow Model Registry webhook to automatically transition the model version to 'Staging' when a new version is registered.
Why it's wrong here
Webhooks can trigger external actions on model version transitions, but they cannot enforce a metric threshold before transitioning. The webhook fires after the transition occurs, not before. It also cannot conditionally transition based on validation accuracy. This approach lacks the required gating logic and would not prevent low-accuracy models from reaching Staging.
- ✗
Use the Databricks REST API to update the model version stage after the job completes, relying on the job's success or failure to indicate accuracy.
Why it's wrong here
The Databricks REST API can update model version stages, but it does not automatically evaluate the accuracy metric. The job's success or failure must be determined by custom logic. Without a conditional check, the stage transition would occur regardless of accuracy. This approach does not enforce the threshold and could promote underperforming models.
- ✗
Set the model version's stage to 'Staging' during registration using the stage parameter in mlflow.register_model, and rely on the training script to skip registration if accuracy is low.
Why it's wrong here
The mlflow.register_model function can set a stage at registration time, but it does not evaluate metrics. Skipping registration based on accuracy requires custom logic in the training script. If the script does not implement that check, the model will be registered and staged regardless of performance. This method does not provide the conditional gating needed.
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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.