Databricks-ML-Assoc Databricks Machine Learning Practice Question
A machine learning engineer is troubleshooting a Model Registry issue where models are not being transitioned correctly. Which TWO actions should the engineer take to ensure proper governance and automated testing in the Registry?
⚠ Common exam trap
Candidates often rely on manual transitions in the Registry, which lacks auditability. They fail to implement automated webhooks or tags, which are required for robust, repeatable CI/CD pipelines.
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
✓
Implement Webhooks to trigger external CI/CD validation pipelines upon status transitions.
Effective model governance relies on robust testing and clear transition protocols. Integrating CI/CD pipelines ensures that models undergo automated unit and integration tests before transitioning to Staging or Production. By utilizing Webhooks and automated transition triggers, teams can enforce validation checks, ensuring only high-quality models reach production. This systematic approach reduces risk and maintains a clear audit trail for compliance and operational reliability within the Databricks ML ecosystem.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Implement Webhooks to trigger external CI/CD validation pipelines upon status transitions.
Why this is correct
Webhooks allow Databricks to trigger external services like Jenkins or GitHub Actions whenever a model version changes state. This enables automated testing and validation workflows, ensuring that models meet performance benchmarks and quality gates before being approved for staging or production, which is a critical governance requirement.
- ✗
Delete all older versions of the model to keep the registry clean and performant.
Why it's wrong here
Deleting older model versions breaks lineage and eliminates the ability to perform rollbacks. Governance practices dictate that model versions should be archived or kept for audit purposes, not deleted, to maintain a history of changes, performance metrics, and data lineage required for regulatory compliance and debugging.
- ✗
Manually update the model stage via the UI for every version to ensure maximum control.
Why it's wrong here
Manual updates are inefficient and error-prone in production environments. Automation is preferred for scaling model delivery. Relying on manual intervention prevents the implementation of consistent validation checks, making it difficult to maintain reliable deployment pipelines and increasing the risk of human oversight in the model lifecycle.
- ✓
Use Model Registry tags to perform metadata-based filtering for downstream automated testing.
Why this is correct
Tags provide a powerful mechanism to annotate model versions with metadata such as 'test_passed', 'model_owner', or 'data_version'. Automated pipelines can query these tags to determine if a model is ready for transition, providing a scalable and metadata-driven approach to model governance and lifecycle management.
- ✗
Disable the model versioning feature to save storage space in the DBFS.
Why it's wrong here
Disabling versioning undermines the fundamental purpose of the Model Registry, which is to provide version control and lineage. Model versions are essential for tracking improvements, comparing performance across iterations, and managing deployments. Removing this capability makes the system non-functional for modern machine learning workflows and production deployments.
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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.