Databricks-ML-Pro ML Ops Practice Question
Your team uses Databricks for machine learning and needs to ensure that model training is fully automated and reproducible. Which THREE of the following are necessary components for a production-grade automated ML pipeline?
⚠ Common exam trap
Candidates often include 'manual monitoring' or 'local testing' as key components, failing to identify the three pillars of automation: Git, Workflows, and the Model Registry.
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
✓
Git integration to version control the training notebooks and scripts.
An automated pipeline requires a workflow orchestrator (Workflows), a centralized registry (MLflow), and version-controlled logic (Git). These three components work together to provide a seamless transition from code development to production deployment. This triad ensures that every step is reproducible, monitored, and audit-ready, which is the foundational requirement for any mature MLOps practice operating at enterprise scale.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Git integration to version control the training notebooks and scripts.
Why this is correct
Version control is fundamental to MLOps. It allows teams to track changes, collaborate, and revert to known good states. Without Git, it is impossible to audit the evolution of training logic or ensure that the code running in production is exactly the same as what was tested during the CI process.
- ✓
Databricks Workflows to schedule and orchestrate pipeline steps.
Why this is correct
Orchestration is critical for automating multi-step processes like data preparation, feature engineering, training, and model evaluation. Databricks Workflows ensures that these steps run in the correct order, with proper dependency management, and provides monitoring and alerting if any part of the pipeline fails during execution.
- ✓
MLflow Model Registry to manage model versions and deployment lifecycle.
Why this is correct
The registry provides the governance layer for models. It ensures that only validated versions move between environments. By tracking the lineage of every model version, it provides the auditability required for enterprise compliance and enables automated promotion patterns that are safe and repeatable within a CI/CD workflow.
- ✗
Manual approval steps for every single model training run.
Why it's wrong here
Manual approval for every training run creates significant bottlenecks and defeats the purpose of an automated pipeline. While human oversight is necessary for final production deployment, the training process itself should be fully automated, allowing for rapid experimentation and iteration without waiting for manual intervention at every step.
- ✗
Using the same cluster for both development and production tasks to minimize complexity.
Why it's wrong here
Mixing development and production environments is a major security and reliability risk. Production tasks should always run on isolated, job-specific compute to prevent accidental interference from development activities. Segregation of environments is a basic requirement for maintaining system stability and security in professional machine learning operations.
About these practice questions
This Databricks-ML-Pro question is part of Courseiva's 300-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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.