Databricks-ML-Assoc ML Workflows Practice Question
Which workflow step is essential before promoting a model from 'Staging' to 'Production' in the MLflow Model Registry?
⚠ Common exam trap
Students often select administrative tasks like registering the model name or creating clusters as the essential step, forgetting that rigorous validation on a hold-out test set is mandatory 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
✓
Validating the model performance on a hold-out test set.
Validation against a held-out test set is a mandatory step before any model promotion. This ensures that the model meets performance requirements and hasn't regressed compared to the currently deployed version. This gated approach, combined with the Model Registry's staging transitions, provides a controlled environment for testing, ensuring that only verified, high-quality models enter the production ecosystem, thus mitigating business risk and maintaining model predictive accuracy in critical applications.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Deleting the original training dataset to save storage space.
Why it's wrong here
Deleting the training dataset is a dangerous practice that prevents model auditing, retraining, and debugging. Data lineage requires that training datasets be preserved, ideally linked to the model version, to ensure that the development process is fully reproducible and that the model's behavior can be investigated if issues arise.
- ✓
Validating the model performance on a hold-out test set.
Why this is correct
Validation on a representative hold-out dataset confirms that the model generalizes well to unseen data. This step prevents the deployment of overfitted models that might perform well on training data but fail in production, acting as a critical quality gate that maintains the reliability of the overall MLOps system.
- ✗
Manually updating the production database credentials in the script.
Why it's wrong here
Hardcoding credentials or manually updating them is a security violation and an anti-pattern. Credentials should be managed through secure secrets management services like Databricks Secrets. This ensures that sensitive information is never exposed in the code or logs, protecting the organization's infrastructure and data from unauthorized access or accidental leakage.
- ✗
Restarting the cluster to clear the memory of the training environment.
Why it's wrong here
Restarting the cluster is unrelated to the model validation process or the logic required for model promotion. While cluster hygiene is generally good, it does not serve as a quality gate. Model promotion should be based on performance metrics, automated testing results, and stakeholder approval, not on clearing memory caches.
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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.