Databricks-ML-Pro ML Ops Practice Question
You are implementing a CI/CD pipeline for a machine learning model on Databricks. The pipeline must automatically retrain the model when new data arrives, validate its performance, and promote it to production if it meets quality thresholds. Which TWO of the following steps are essential to include in the pipeline to ensure safe and automated deployment? (Choose two.)
⚠ Common exam trap
The trap here is thinking that manual review or direct overwrite can be part of an automated pipeline, but automation requires objective, reproducible validation steps without human intervention.
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 automated tests that evaluate the model on a holdout dataset and compare metrics against a baseline before promotion.
The essential steps for an automated CI/CD pipeline include registering the model in MLflow Model Registry with stage transitions and implementing automated tests on a holdout dataset to validate performance against a baseline. These steps ensure that only models meeting quality thresholds are promoted, enabling safe automation. Other steps like manual review or storing artifacts in Git are not suitable for automated pipelines.
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 automated tests that evaluate the model on a holdout dataset and compare metrics against a baseline before promotion.
Why this is correct
Automated tests on a holdout dataset verify that the model meets performance thresholds before promotion. Comparing against a baseline ensures that the new model does not regress. This step is essential for safe automation, as it gates deployment on objective criteria and prevents degradation in production.
- ✗
Manually review the model's performance metrics in a notebook before allowing the pipeline to proceed.
Why it's wrong here
Manual review introduces delays and human error, contradicting the goal of an automated CI/CD pipeline. While manual review can be useful for high-risk models, the requirement is for automated retraining and promotion based on thresholds. Relying on manual steps reduces the efficiency and reliability of the pipeline.
- ✗
Store the model artifacts in a Git repository alongside the code to ensure versioning.
Why it's wrong here
Git is not designed for large binary artifacts like model files; it can bloat the repository and slow down operations. MLflow Model Registry or Unity Catalog volumes are better suited for model versioning and storage. Using Git for artifacts can lead to performance issues and does not provide the same governance features.
- ✗
Configure the pipeline to directly overwrite the production model endpoint with the newly trained model without validation to reduce latency.
Why it's wrong here
Skipping validation risks deploying underperforming or faulty models, which can cause business impact. Automated deployment should always include validation steps to ensure quality. Overwriting without validation bypasses safety checks and is not a best practice for ML Ops, as it can lead to silent failures in production.
- ✓
Use MLflow Model Registry to register the model and transition it to 'Staging' for validation, then to 'Production' after passing tests.
Why this is correct
MLflow Model Registry provides a centralized model store with stage transitions, enabling automated promotion based on validation results. Registering the model and moving it through stages ensures that only validated models reach production. This is a core component of ML Ops CI/CD for models, as it provides versioning, lineage, and approval workflows.
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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.